Awesome-CVPR2024-AIGC

A Collection of Papers and Codes for CVPR2024 AIGC

整理汇总下今年CVPR AIGC相关的论文和代码，具体如下。

欢迎star，fork和PR~
优先在Github更新：Awesome-CVPR2024-AIGC，欢迎star~
知乎：https://zhuanlan.zhihu.com/p/684325134

参考或转载请注明出处

CVPR2024官网：https://cvpr.thecvf.com/Conferences/2024

CVPR接收论文列表：https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers

CVPR完整论文库：https://openaccess.thecvf.com/CVPR2024

开会时间：2024年6月17日-6月21日

论文接收公布时间：2024年2月27日

【Contents】

• 1.图像生成(Image Generation/Image Synthesis)
• 2.图像编辑（Image Editing)
• 3.视频生成(Video Generation/Image Synthesis)
• 4.视频编辑(Video Editing)
• 5.3D生成(3D Generation/3D Synthesis)
• 6.3D编辑(3D Editing)
• 7.多模态大语言模型(Multi-Modal Large Language Model)
• 8.其他多任务(Others)

1.图像生成(Image Generation/Image Synthesis)

Accelerating Diffusion Sampling with Optimized Time Steps

• Paper: https://arxiv.org/abs/2402.17376
• Code: https://github.com/scxue/DM-NonUniform

Adversarial Text to Continuous Image Generation

• Paper: https://openreview.net/forum?id=9X3UZJSGIg9
• Code:

Amodal Completion via Progressive Mixed Context Diffusion

• Paper: https://arxiv.org/abs/2312.15540
• Code: https://github.com/k8xu/amodal

Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder

• Paper: https://arxiv.org/abs/2403.10255
• Code: https://github.com/zhenshij/arbitrary-scale-diffusion

Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion

• Paper: https://arxiv.org/abs/2312.12471
• Code: https://github.com/zkawfanx/Atlantis

Attention Calibration for Disentangled Text-to-Image Personalization

• Paper: https://arxiv.org/abs/2403.18551
• Code: https://github.com/Monalissaa/DisenDiff

Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models

• Paper: https://arxiv.org/abs/2405.05252
• Code:

CapHuman: Capture Your Moments in Parallel Universes

• Paper: https://arxiv.org/abs/2402.18078
• Code: https://github.com/VamosC/CapHuman

CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization

• Paper: https://arxiv.org/abs/2404.00521
• Code:

Check, Locate, Rectify: A Training-Free Layout Calibration System for Text-to-Image Generation

• Paper: https://arxiv.org/abs/2311.15773
• Code:

Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

• Paper: https://arxiv.org/abs/2402.00627
• Code: https://github.com/YanzuoLu/CFLD

CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation

• Paper: https://arxiv.org/abs/2310.01407
• Code: https://github.com/fast-codi/CoDi

Condition-Aware Neural Network for Controlled Image Generation

• Paper: https://arxiv.org/abs/2404.01143v1
• Code:

CosmicMan: A Text-to-Image Foundation Model for Humans

• Paper: https://arxiv.org/abs/2404.01294
• Code: https://github.com/cosmicman-cvpr2024/CosmicMan

Countering Personalized Text-to-Image Generation with Influence Watermarks

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Countering_Personalized_Text-to-Image_Generation_with_Influence_Watermarks_CVPR_2024_paper.html
• Code:

Cross Initialization for Face Personalization of Text-to-Image Models

• Paper: https://arxiv.org/abs/2312.15905
• Code: https://github.com/lyuPang/CrossInitialization

Customization Assistant for Text-to-image Generation

• Paper: https://arxiv.org/abs/2312.03045
• Code:

DeepCache: Accelerating Diffusion Models for Free

• Paper: https://arxiv.org/abs/2312.00858
• Code: https://github.com/horseee/DeepCache

DemoFusion: Democratising High-Resolution Image Generation With No $

• Paper: https://arxiv.org/abs/2311.16973
• Code: https://github.com/PRIS-CV/DemoFusion

Desigen: A Pipeline for Controllable Design Template Generation

• Paper: https://arxiv.org/abs/2403.09093
• Code: https://github.com/whaohan/desigen

DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model

• Paper: https://arxiv.org/abs/2404.01342
• Code: https://github.com/OpenGVLab/DiffAgent

Diffusion-driven GAN Inversion for Multi-Modal Face Image Generation

• Paper: https://arxiv.org/abs/2405.04356v1
• Code:

DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models

• Paper: https://arxiv.org/abs/2402.19481
• Code: https://github.com/mit-han-lab/distrifuser

Diversity-aware Channel Pruning for StyleGAN Compression

• Paper: https://arxiv.org/abs/2403.13548
• Code: https://github.com/jiwoogit/DCP-GAN

Discriminative Probing and Tuning for Text-to-Image Generation

• Paper: https://www.arxiv.org/abs/2403.04321
• Code: https://github.com/LgQu/DPT-T2I

Don’t drop your samples! Coherence-aware training benefits Conditional diffusion

• Paper: https://arxiv.org/abs/2405.20324
• Code: https://github.com/nicolas-dufour/CAD

Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation

• Paper: https://arxiv.org/abs/2404.01050
• Code: https://github.com/haofengl/DragNoise

DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization

• Paper: https://arxiv.org/abs/2402.09812
• Code: https://github.com/KU-CVLAB/DreamMatcher

Dynamic Prompt Optimizing for Text-to-Image Generation

• Paper: https://arxiv.org/abs/2404.04095
• Code: https://github.com/Mowenyii/PAE

ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations

• Paper: https://arxiv.org/abs/2312.04655
• Code: https://github.com/eclipse-t2i/eclipse-inference

Efficient Dataset Distillation via Minimax Diffusion

• Paper: https://arxiv.org/abs/2311.15529
• Code: https://github.com/vimar-gu/MinimaxDiffusion

ElasticDiffusion: Training-free Arbitrary Size Image Generation

• Paper: https://arxiv.org/abs/2311.18822
• Code: https://github.com/MoayedHajiAli/ElasticDiffusion-official

EmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models

• Paper: https://arxiv.org/abs/2401.04608
• Code: https://github.com/JingyuanYY/EmoGen

Enabling Multi-Concept Fusion in Text-to-Image Models

• Paper: https://arxiv.org/abs/2404.03913v1
• Code:

Exact Fusion via Feature Distribution Matching for Few-shot Image Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Exact_Fusion_via_Feature_Distribution_Matching_for_Few-shot_Image_Generation_CVPR_2024_paper.html
• Code:

FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation

• Paper: https://arxiv.org/abs/2403.06775
• Code:

Fast ODE-based Sampling for Diffusion Models in Around 5 Steps

• Paper: https://arxiv.org/abs/2312.00094
• Code: https://github.com/zju-pi/diff-sampler

FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition

• Paper: https://arxiv.org/abs/2312.07536
• Code: https://github.com/genforce/freecontrol

FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition

• Paper: https://arxiv.org/abs/2405.13870
• Code: https://github.com/aim-uofa/FreeCustom

Generalizable Tumor Synthesis

• Paper: https://www.cs.jhu.edu/~alanlab/Pubs24/chen2024towards.pdf
• Code: https://github.com/MrGiovanni/DiffTumor

Generating Daylight-driven Architectural Design via Diffusion Models

• Paper: https://arxiv.org/abs/2404.13353
• Code: https://github.com/unlimitedli/DDADesign

Generative Unlearning for Any Identity

• Paper: https://arxiv.org/abs/2405.09879
• Code: https://github.com/JJuOn/GUIDE

HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances

• Paper: https://arxiv.org/abs/2403.01693
• Code: https://github.com/JJuOn/GUIDE

High-fidelity Person-centric Subject-to-Image Synthesis

• Paper: https://arxiv.org/abs/2311.10329
• Code: https://github.com/CodeGoat24/Face-diffuser?tab=readme-ov-file

InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

• Paper: https://arxiv.org/abs/2404.04650
• Code: https://github.com/xiefan-guo/initno

InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning

• Paper: https://arxiv.org/abs/2304.03411
• Code:

InstanceDiffusion: Instance-level Control for Image Generation

• Paper: https://arxiv.org/abs/2402.03290
• Code: https://github.com/frank-xwang/InstanceDiffusion

Instruct-Imagen: Image Generation with Multi-modal Instruction

• Paper: https://arxiv.org/abs/2401.01952
• Code:

Intelligent Grimm - Open-ended Visual Storytelling via Latent Diffusion Models

• Paper: https://arxiv.org/abs/2306.00973
• Code: https://github.com/haoningwu3639/StoryGen

InteractDiffusion: Interaction-Control for Text-to-Image Diffusion Model

• Paper: https://arxiv.org/abs/2312.05849
• Code: https://github.com/jiuntian/interactdiffusion

Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models

• Paper: https://arxiv.org/abs/2308.15692
• Code:

Inversion-Free Image Editing with Natural Language

• Paper: https://arxiv.org/abs/2312.04965
• Code: https://github.com/sled-group/InfEdit

JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_JeDi_Joint-Image_Diffusion_Models_for_Finetuning-Free_Personalized_Text-to-Image_Generation_CVPR_2024_paper.html
• Code:

LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion

• Paper: https://arxiv.org/abs/2404.00292
• Code: https://github.com/PanchengZhao/LAKE-RED

Learned representation-guided diffusion models for large-image generation

• Paper: https://arxiv.org/abs/2312.07330
• Code: https://github.com/cvlab-stonybrook/Large-Image-Diffusion

Learning Continuous 3D Words for Text-to-Image Generation

• Paper: https://arxiv.org/abs/2402.08654
• Code: https://github.com/ttchengab/continuous_3d_words_code/

Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation

• Paper: https://arxiv.org/abs/2311.15841
• Code:

Learning Multi-dimensional Human Preference for Text-to-Image Generation

• Paper: https://arxiv.org/abs/2311.15841
• Code:

LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion Model

• Paper: https://arxiv.org/abs/2305.11577
• Code: https://github.com/ewrfcas/LeftRefill

MACE: Mass Concept Erasure in Diffusion Models

• Paper: https://arxiv.org/abs/2402.05408
• Code: https://github.com/Shilin-LU/MACE

MarkovGen: Structured Prediction for Efficient Text-to-Image Generation

• Paper: https://arxiv.org/abs/2308.10997
• Code:

MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant

• Paper: https://arxiv.org/abs/2403.04290
• Code:

MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis

• Paper: https://arxiv.org/abs/2402.05408
• Code: https://github.com/limuloo/MIGC

MindBridge: A Cross-Subject Brain Decoding Framework

• Paper: https://arxiv.org/abs/2404.07850
• Code: https://github.com/littlepure2333/MindBridge

MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation

• Paper: https://arxiv.org/abs/2404.02790
• Code: https://huggingface.co/datasets/mulan-dataset/v1.0

On the Scalability of Diffusion-based Text-to-Image Generation

• Paper: https://arxiv.org/abs/2404.02883
• Code:

OpenBias: Open-set Bias Detection in Text-to-Image Generative Models

• Paper: https://arxiv.org/abs/2404.07990
• Code: https://github.com/Picsart-AI-Research/OpenBias

Personalized Residuals for Concept-Driven Text-to-Image Generation

• Paper: https://arxiv.org/abs/2405.12978
• Code:

Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models

• Paper: https://arxiv.org/abs/2404.15081
• Code:

PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding

• Paper: https://arxiv.org/abs/2312.04461
• Code: https://github.com/TencentARC/PhotoMaker

PLACE: Adaptive Layout-Semantic Fusion for Semantic Image Synthesis

• Paper: https://arxiv.org/abs/2403.01852
• Code: https://github.com/cszy98/PLACE

Plug-and-Play Diffusion Distillation

• Paper: https://arxiv.org/abs/2406.01954
• Code:

Prompt-Free Diffusion: Taking “Text” out of Text-to-Image Diffusion Models

• Paper: https://arxiv.org/abs/2305.16223
• Code: https://github.com/SHI-Labs/Prompt-Free-Diffusion

Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following

• Paper: https://arxiv.org/abs/2311.17002
• Code: https://github.com/ali-vilab/Ranni

Readout Guidance: Learning Control from Diffusion Features

• Paper: https://arxiv.org/abs/2312.02150
• Code: https://github.com/google-research/readout_guidance

Relation Rectification in Diffusion Model

• Paper: https://arxiv.org/abs/2403.20249
• Code: https://github.com/WUyinwei-hah/RRNet

Residual Denoising Diffusion Models

• Paper: https://arxiv.org/abs/2308.13712
• Code: https://github.com/nachifur/RDDM

Rethinking FID: Towards a Better Evaluation Metric for Image Generation

• Paper: https://arxiv.org/abs/2401.09603
• Code: https://github.com/google-research/google-research/tree/master/cmmd

Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance

• Paper: https://arxiv.org/abs/2404.05384
• Code: https://github.com/SmilesDZgk/S-CFG

Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation

• Paper: https://arxiv.org/abs/2311.13602
• Code: https://github.com/CyberAgentAILab/RALF

Rich Human Feedback for Text-to-Image Generation

• Paper: https://arxiv.org/abs/2312.10240
• Code:

SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation

• Paper: https://arxiv.org/abs/2401.08053
• Code:

Self-correcting LLM-controlled Diffusion Models

• Paper: https://arxiv.org/abs/2311.16090
• Code: https://github.com/tsunghan-wu/SLD

Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation

• Paper: https://arxiv.org/abs/2311.17216
• Code: https://github.com/hangligit/InterpretDiffusion

Shadow Generation for Composite Image Using Diffusion Model

• Paper: https://arxiv.org/abs/2308.09972
• Code: https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2

Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models

• Paper: https://arxiv.org/abs/2312.04410
• Code: https://github.com/SHI-Labs/Smooth-Diffusion

SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation

• Paper: https://arxiv.org/abs/2312.16272
• Code: https://github.com/Xiaojiu-z/SSR_Encoder

StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

• Paper: https://arxiv.org/abs/2312.01725
• Code: https://github.com/rlawjdghek/StableVITON

Structure-Guided Adversarial Training of Diffusion Models

• Paper: https://arxiv.org/abs/2402.17563
• Code:

Style Aligned Image Generation via Shared Attention

• Paper: https://arxiv.org/abs/2312.02133
• Code: https://github.com/google/style-aligned/

SVGDreamer: Text Guided SVG Generation with Diffusion Model

• Paper: https://arxiv.org/abs/2312.16476
• Code: https://github.com/ximinng/SVGDreamer

SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation

• Paper: https://arxiv.org/abs/2312.05239
• Code: https://github.com/VinAIResearch/SwiftBrush

Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting

• Paper: https://arxiv.org/abs/2310.08129
• Code: https://github.com/zzjchen/Tailored-Visions

Tackling the Singularities at the Endpoints of Time Intervals in Diffusion Models

• Paper: https://arxiv.org/abs/2403.08381
• Code: https://github.com/PangzeCheung/SingDiffusion

Taming Stable Diffusion for Text to 360∘ Panorama Image Generation

• Paper: https://arxiv.org/abs/2404.07949
• Code: https://github.com/chengzhag/PanFusion

TextCraftor: Your Text Encoder Can be Image Quality Controller

• Paper: https://arxiv.org/abs/2403.18978
• Code:

Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation

• Paper: https://arxiv.org/abs/2403.06247
• Code: https://github.com/MingyuLee82/TGI_AD_v1

TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models

• Paper: https://arxiv.org/abs/2311.16503
• Code: https://github.com/ModelTC/TFMQ-DM

TokenCompose: Grounding Diffusion with Token-level Supervision

• Paper: https://arxiv.org/abs/2312.03626
• Code: https://github.com/mlpc-ucsd/TokenCompose

Towards Accurate Post-training Quantization for Diffusion Models

• Paper: https://arxiv.org/abs/2305.18723
• Code: https://github.com/ChangyuanWang17/APQ-DM

Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation

• Paper: https://arxiv.org/abs/2403.05239
• Code:

Towards Memorization-Free Diffusion Models

• Paper: https://arxiv.org/abs/2404.00922
• Code: https://github.com/chenchen-usyd/AMG

Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Miao_Training_Diffusion_Models_Towards_Diverse_Image_Generation_with_Reinforcement_Learning_CVPR_2024_paper.html
• Code:

UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs

• Paper: https://arxiv.org/abs/2311.09257
• Code:

UniGS: Unified Representation for Image Generation and Segmentation

• Paper: https://arxiv.org/abs/2312.01985
• Code: https://github.com/qqlu/Entity

Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model

• Paper: https://arxiv.org/abs/2311.13231
• Code: https://github.com/yk7333/d3po

U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation

• Paper: https://arxiv.org/abs/2403.20231
• Code: https://github.com/ICTMCG/U-VAP

ViewDiff: 3D-Consistent Image Generation with Text-To-Image Models

• Paper: https://arxiv.org/abs/2403.01807
• Code: https://github.com/facebookresearch/ViewDiff

When StyleGAN Meets Stable Diffusion: a 𝒲+ Adapter for Personalized Image Generation

• Paper: https://arxiv.org/abs/2311.17461
• Code: https://github.com/csxmli2016/w-plus-adapter

X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model

• Paper: https://arxiv.org/abs/2312.02238
• Code: https://github.com/showlab/X-Adapter

2.图像编辑(Image Editing)

An Edit Friendly DDPM Noise Space: Inversion and Manipulations

• Paper: https://arxiv.org/abs/2304.06140
• Code: https://github.com/inbarhub/DDPM_inversion

Choose What You Need: Disentangled Representation Learning for Scene Text Recognition, Removal and Editing

• Paper: https://arxiv.org/abs/2405.04377
• Code:

Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Content-Style_Decoupling_for_Unsupervised_Makeup_Transfer_without_Generating_Pseudo_Ground_CVPR_2024_paper.html
• Code: https://github.com/Snowfallingplum/CSD-MT

Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing

• Paper: https://arxiv.org/abs/2311.18608
• Code: https://github.com/HyelinNAM/ContrastiveDenoisingScore

DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations

• Paper: https://arxiv.org/abs/2403.06951
• Code: https://github.com/Tianhao-Qi/DEADiff_code

Deformable One-shot Face Stylization via DINO Semantic Guidance

• Paper: https://arxiv.org/abs/2403.00459
• Code: https://github.com/zichongc/DoesFS

DemoCaricature: Democratising Caricature Generation with a Rough Sketch

• Paper: https://arxiv.org/abs/2312.04364
• Code: https://github.com/ChenDarYen/DemoCaricature

DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Sun_DiffAM_Diffusion-based_Adversarial_Makeup_Transfer_for_Facial_Privacy_Protection_CVPR_2024_paper.html
• Code: https://github.com/HansSunY/DiffAM

DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing

• Paper: https://arxiv.org/abs/2312.07409
• Code: https://github.com/Kevin-thu/DiffMorpher

Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D

• Paper: https://arxiv.org/abs/2312.02190
• Code: https://github.com/adobe-research/DiffusionHandles

DiffusionLight: Light Probes for Free by Painting a Chrome Ball

• Paper: https://arxiv.org/abs/2312.09168
• Code: https://github.com/DiffusionLight/DiffusionLight

Diffusion Models Without Attention

• Paper: https://arxiv.org/abs/2311.18257
• Code: https://github.com/Kevin-thu/DiffMorpher

Doubly Abductive Counterfactual Inference for Text-based Image Editing

• Paper: https://arxiv.org/abs/2403.02981
• Code: https://github.com/xuesong39/DAC

Edit One for All: Interactive Batch Image Editing

• Paper: https://arxiv.org/abs/2401.10219
• Code: https://github.com/thaoshibe/edit-one-for-all

Face2Diffusion for Fast and Editable Face Personalization

• Paper: https://arxiv.org/abs/2403.05094
• Code: https://github.com/mapooon/Face2Diffusion

Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation

• Paper: https://arxiv.org/abs/2312.10113
• Code: https://github.com/guoqincode/Focus-on-Your-Instruction

FreeDrag: Feature Dragging for Reliable Point-based Image Editing

• Paper: https://arxiv.org/abs/2307.04684
• Code: https://github.com/LPengYang/FreeDrag

Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image

• Paper: https://arxiv.org/abs/2403.09632
• Code: https://github.com/guoqincode/Focus-on-Your-Instruction

Image Sculpting: Precise Object Editing with 3D Geometry Control

• Paper: https://arxiv.org/abs/2401.01702
• Code: https://github.com/vision-x-nyu/image-sculpting

Inversion-Free Image Editing with Natural Language

• Paper: hhttps://arxiv.org/abs/2312.04965
• Code: https://github.com/sled-group/InfEdit

PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models

• Paper: https://arxiv.org/abs/2303.17546
• Code: https://github.com/Picsart-AI-Research/PAIR-Diffusion

Person in Place: Generating Associative Skeleton-Guidance Maps for Human-Object Interaction Image Editing

• Paper: https://arxiv.org/abs/2303.17546
• Code: https://github.com/YangChangHee/CVPR2024_Person-In-Place_RELEASE?tab=readme-ov-file

Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network

• Paper: https://arxiv.org/abs/2405.19775
• Code:

PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models

• Paper: https://arxiv.org/abs/2312.13964
• Code: https://github.com/open-mmlab/PIA

RealCustom: Narrowing Real Text Word for Real-Time Open-Domain Text-to-Image Customization

• Paper: https://arxiv.org/abs/2403.00483
• Code:

SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models

• Paper: https://arxiv.org/abs/2312.06739
• Code: https://github.com/TencentARC/SmartEdit

Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer

• Paper: https://arxiv.org/abs/2312.09008
• Code: https://github.com/jiwoogit/StyleID

SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting

• Paper: https://arxiv.org/abs/2402.18848
• Code:

Text-Driven Image Editing via Learnable Regions

• Paper: https://arxiv.org/abs/2311.16432
• Code: https://github.com/yuanze-lin/Learnable_Regions

Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On

• Paper: https://arxiv.org/abs/2404.01089
• Code: https://github.com/Gal4way/TPD

TiNO-Edit: Timestep and Noise Optimization for Robust Diffusion-Based Image Editing

• Paper: https://arxiv.org/abs/2404.11120
• Code: https://github.com/SherryXTChen/TiNO-Edit

UniHuman: A Unified Model For Editing Human Images in the Wild

• Paper: https://arxiv.org/abs/2312.14985
• Code: https://github.com/NannanLi999/UniHuman

ZONE: Zero-Shot Instruction-Guided Local Editing

• Paper: https://arxiv.org/abs/2312.16794
• Code: https://github.com/lsl001006/ZONE

3.视频生成(Video Generation/Video Synthesis)

360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model

• Paper: https://arxiv.org/abs/2401.06578
• Code: https://github.com/Akaneqwq/360DVD

A Recipe for Scaling up Text-to-Video Generation with Text-free Videos

• Paper: https://arxiv.org/abs/2312.15770
• Code: https://github.com/ali-vilab/VGen

BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models

• Paper: https://arxiv.org/abs/2312.02813
• Code: https://github.com/MCG-NJU/BIVDiff

ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis

• Paper: https://arxiv.org/abs/2403.17936
• Code: https://github.com/m-hamza-mughal/convofusion

Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model

• Paper: https://arxiv.org/abs/2404.01862
• Code: https://github.com/thuhcsi/S2G-MDDiffusion

DiffPerformer: Iterative Learning of Consistent Latent Guidance for Diffusion-based Human Video Generation

• Paper:
• Code:

DisCo: Disentangled Control for Realistic Human Dance Generation

• Paper: https://arxiv.org/abs/2307.00040
• Code: https://github.com/Wangt-CN/DisCo

FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio

• Paper: https://arxiv.org/abs/2403.01901
• Code:

Faces that Speak: Jointly Synthesising Talking Face and Speech from Text

• Paper: https://arxiv.org/abs/2405.10272
• Code: https://github.com/Wangt-CN/DisCo

FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Liang_FlowVid_Taming_Imperfect_Optical_Flows_for_Consistent_Video-to-Video_Synthesis_CVPR_2024_paper.html
• Code:

Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Cai_Generative_Rendering_Controllable_4D-Guided_Video_Generation_with_2D_Diffusion_Models_CVPR_2024_paper.html
• Code:

GenTron: Diffusion Transformers for Image and Video Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Chen_GenTron_Diffusion_Transformers_for_Image_and_Video_Generation_CVPR_2024_paper.html
• Code:

Grid Diffusion Models for Text-to-Video Generation

• Paper: https://arxiv.org/abs/2404.00234
• Code: https://github.com/taegyeong-lee/Grid-Diffusion-Models-for-Text-to-Video-Generation

Hierarchical Patch-wise Diffusion Models for High-Resolution Video Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Skorokhodov_Hierarchical_Patch_Diffusion_Models_for_High-Resolution_Video_Generation_CVPR_2024_paper.html
• Code:

Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Qing_Hierarchical_Spatio-temporal_Decoupling_for_Text-to-Video_Generation_CVPR_2024_paper.html
• Code:

LAMP: Learn A Motion Pattern for Few-Shot Video Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Wu_LAMP_Learn_A_Motion_Pattern_for_Few-Shot_Video_Generation_CVPR_2024_paper.html
• Code: https://github.com/RQ-Wu/LAMP

Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis

• Paper: https://arxiv.org/abs/2402.17364
• Code: https://github.com/zhangzc21/DynTet

Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation guided by the Characteristic Dance Primitives

• Paper: https://arxiv.org/abs/2403.10518
• Code: https://github.com/li-ronghui/LODGE

MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model

• Paper: https://arxiv.org/abs/2311.16498
• Code: https://github.com/magic-research/magic-animate

Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework

• Paper: https://arxiv.org/abs/2403.16510
• Code: https://github.com/ICTMCG/Make-Your-Anchor

Make Your Dream A Vlog

• Paper: https://arxiv.org/abs/2401.09414
• Code: https://github.com/Vchitect/Vlogger

Make Pixels Dance: High-Dynamic Video Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_Make_Pixels_Dance_High-Dynamic_Video_Generation_CVPR_2024_paper.html
• Code:

MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Wang_MicroCinema_A_Divide-and-Conquer_Approach_for_Text-to-Video_Generation_CVPR_2024_paper.html
• Code:

Panacea: Panoramic and Controllable Video Generation for Autonomous Driving

• Paper: https://arxiv.org/abs/2311.16813
• Code: https://github.com/wenyuqing/panacea

PEEKABOO: Interactive Video Generation via Masked-Diffusion

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Jain_PEEKABOO_Interactive_Video_Generation_via_Masked-Diffusion_CVPR_2024_paper.html
• Code:

Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners

• Paper: https://arxiv.org/abs/2308.13712
• Code: https://github.com/yzxing87/Seeing-and-Hearing

SimDA: Simple Diffusion Adapter for Efficient Video Generation

• Paper: https://arxiv.org/abs/2308.09710
• Code: https://github.com/ChenHsing/SimDA

StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN

• Paper: https://arxiv.org/abs/2403.14186
• Code: https://github.com/jeolpyeoni/StyleCineGAN

SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis

• Paper: https://arxiv.org/abs/2311.17590
• Code: https://github.com/ZiqiaoPeng/SyncTalk

TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models

• Paper: https://arxiv.org/abs/2311.17590
• Code:

Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

• Paper: https://arxiv.org/abs/2404.16306
• Code: https://github.com/showlab/Tune-A-Video

VideoBooth: Diffusion-based Video Generation with Image Prompts

• Paper: https://arxiv.org/abs/2312.00777
• Code: https://github.com/Vchitect/VideoBooth

VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

• Paper: https://arxiv.org/abs/2401.09047
• Code: https://github.com/AILab-CVC/VideoCrafter

Video-P2P: Video Editing with Cross-attention Control

• Paper: https://arxiv.org/abs/2303.04761
• Code: https://github.com/dvlab-research/Video-P2P

4.视频编辑(Video Editing)

A Video is Worth 256 Bases: Spatial-Temporal Expectation-Maximization Inversion for Zero-Shot Video Editing

• Paper: https://arxiv.org/abs/2312.05856
• Code: https://github.com/STEM-Inv/stem-inv

CAMEL: Causal Motion Enhancement tailored for lifting text-driven video editing

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_CAMEL_CAusal_Motion_Enhancement_Tailored_for_Lifting_Text-driven_Video_Editing_CVPR_2024_paper.html
• Code: https://github.com/zhangguiwei610/CAMEL

CCEdit: Creative and Controllable Video Editing via Diffusion Models

• Paper: https://arxiv.org/abs/2309.16496
• Code: https://github.com/RuoyuFeng/CCEdit

CoDeF: Content Deformation Fields for Temporally Consistent Video Processing

• Paper: https://arxiv.org/abs/2308.07926
• Code: https://github.com/qiuyu96/CoDeF

FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation

• Paper: https://arxiv.org/abs/2403.12962
• Code: https://github.com/williamyang1991/FRESCO/tree/main

RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models

• Paper: https://arxiv.org/abs/2312.04524
• Code: https://github.com/rehg-lab/RAVE

VidToMe: Video Token Merging for Zero-Shot Video Editing

• Paper: https://arxiv.org/abs/2312.10656
• Code: https://github.com/lixirui142/VidToMe

VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models

• Paper: https://arxiv.org/abs/2312.00845
• Code: https://github.com/HyeonHo99/Video-Motion-Customization

5.3D生成(3D Generation/3D Synthesis)

4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

• Paper: https://arxiv.org/abs/2310.08528
• Code: https://github.com/hustvl/4DGaussians

Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling

• Paper: https://arxiv.org/abs/2311.16096
• Code: https://github.com/lizhe00/AnimatableGaussians

A Unified Approach for Text- and Image-guided 4D Scene Generation

• Paper: https://arxiv.org/abs/2311.16854
• Code: https://github.com/NVlabs/dream-in-4d

BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation

• Paper: https://arxiv.org/abs/2405.09546
• Code: https://github.com/behavior-vision-suite/behavior-vision-suite.github.io

BerfScene: Bev-conditioned Equivariant Radiance Fields for Infinite 3D Scene Generation

• Paper: https://arxiv.org/abs/2312.02136
• Code: https://github.com/zqh0253/BerfScene

CAD: Photorealistic 3D Generation via Adversarial Distillation

• Paper: https://arxiv.org/abs/2312.06663
• Code: https://github.com/raywzy/CAD

CAGE: Controllable Articulation GEneration

• Paper: https://arxiv.org/abs/2312.09570
• Code: https://github.com/3dlg-hcvc/cage

CityDreamer: Compositional Generative Model of Unbounded 3D Cities

• Paper: https://arxiv.org/abs/2309.00610
• Code: https://github.com/hzxie/CityDreamer

Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior

• Paper: https://arxiv.org/abs/2401.09050
• Code: https://github.com/sail-sg/Consistent3D

ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis

• Paper: https://arxiv.org/abs/2311.17123
• Code: https://github.com/gaoxiangjun/ConTex-Human

ControlRoom3D: Room Generation using Semantic Proxy Rooms

• Paper： https://arxiv.org/abs/2312.05208
• Code:

DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance

• Paper: https://arxiv.org/abs/2403.13667
• Code: https://github.com/Carmenw1203/DanceCamera3D-Official

DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View Synthesis

• Paper: https://arxiv.org/abs/2312.13016
• Code: https://github.com/FreedomGu/DiffPortrait3D

DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation

• Paper: https://arxiv.org/abs/2401.04747
• Code: https://github.com/JeremyCJM/DiffSHEG

DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis

• Paper: https://arxiv.org/abs/2303.14207
• Code: https://github.com/tangjiapeng/DiffuScene

Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features

• Paper: https://arxiv.org/abs/2311.17024
• Code: https://github.com/niladridutt/Diffusion-3D-Features

Diffusion Time-step Curriculum for One Image to 3D Generation

• Paper: https://paperswithcode.com/paper/diffusion-time-step-curriculum-for-one-image
• Code: https://github.com/yxymessi/DTC123

DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models

• Paper: https://arxiv.org/abs/2304.00916
• Code: https://github.com/yukangcao/DreamAvatar

DreamComposer: Controllable 3D Object Generation via Multi-View Conditions

• Paper: https://arxiv.org/abs/2312.03611
• Code: https://github.com/yhyang-myron/DreamComposer

DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior

• Paper: https://arxiv.org/abs/2312.06439
• Code: https://github.com/tyhuang0428/DreamControl

Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion

• Paper: https://arxiv.org/abs/2312.04466
• Code: https://github.com/kiranchhatre/amuse

EscherNet: A Generative Model for Scalable View Synthesis

• Paper: https://arxiv.org/abs/2402.03908
• Code: https://github.com/hzxie/city-dreamer

GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models

• Paper: https://arxiv.org/abs/2310.08529
• Code: https://github.com/hustvl/GaussianDreamer

GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation

• Paper: https://arxiv.org/abs/2401.04092
• Code: https://github.com/3DTopia/GPTEval3D

Gaussian Shell Maps for Efficient 3D Human Generation

• Paper: https://arxiv.org/abs/2311.17857
• Code: https://github.com/computational-imaging/GSM

HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D

• Paper: https://arxiv.org/abs/2312.15980
• Code: https://github.com/byeongjun-park/HarmonyView

HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding

• Paper: https://arxiv.org/abs/2312.03050
• Code:

Holodeck: Language Guided Generation of 3D Embodied AI Environments

• Paper: https://arxiv.org/abs/2312.09067
• Code: https://github.com/allenai/Holodeck

HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation

• Paper: https://arxiv.org/abs/2310.01406
• Code:

Interactive3D: Create What You Want by Interactive 3D Generation

• Paper: https://hub.baai.ac.cn/paper/494efc8d-f4ed-4ca4-8469-b882f9489f5e
• Code:

InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusio

• Paper: https://arxiv.org/abs/2403.17422
• Code: https://github.com/jyunlee/InterHandGen

Intrinsic Image Diffusion for Single-view Material Estimation

• Paper: https://arxiv.org/abs/2312.12274
• Code: https://github.com/Peter-Kocsis/IntrinsicImageDiffusion

Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from Text

• Paper: https://arxiv.org/abs/2403.16897
• Code: https://github.com/junshutang/Make-It-Vivid

MoMask: Generative Masked Modeling of 3D Human Motions

• Paper: https://arxiv.org/abs/2312.00063
• Code: https://github.com/EricGuo5513/momask-codes

Editable Scene Simulation for Autonomous Driving via LLM-Agent Collaboration

• Paper: https://arxiv.org/abs/2402.05746
• Code: https://github.com/yifanlu0227/ChatSim?tab=readme-ov-file

EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion

• Paper: https://arxiv.org/abs/2312.06725
• Code: https://github.com/huanngzh/EpiDiff

OED: Towards One-stage End-to-End Dynamic Scene Graph Generation

• Paper: https://arxiv.org/abs/2405.16925
• Code:

One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

• Paper: https://arxiv.org/abs/2311.07885
• Code: https://github.com/SUDO-AI-3D/One2345plus

Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering

• Paper: https://arxiv.org/abs/2312.11360
• Code: https://github.com/postech-ami/Paint-it

PEGASUS: Personalized Generative 3D Avatars with Composable Attributes

• Paper: https://arxiv.org/abs/2402.10636
• Code: https://github.com/snuvclab/pegasus

PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics

• Paper: https://arxiv.org/abs/2311.12198
• Code: https://github.com/XPandora/PhysGaussian

RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D.

• Paper: https://arxiv.org/abs/2311.16918
• Code: https://github.com/modelscope/richdreamer

SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors

• Paper: https://arxiv.org/abs/2311.17261
• Code: https://github.com/daveredrum/SceneTex

SceneWiz3D: Towards Text-guided 3D Scene Composition

• Paper: https://arxiv.org/abs/2312.08885
• Code: https://github.com/zqh0253/SceneWiz3D

SemCity: Semantic Scene Generation with Triplane Diffusion

• Paper: https://arxiv.org/abs/2403.07773
• Code: https://github.com/zoomin-lee/SemCity?tab=readme-ov-file

Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior

• Paper: https://arxiv.org/abs/2312.06655
• Code: https://github.com/liuff19/Sherpa3D

SIGNeRF: Scene Integrated Generation for Neural Radiance Fields

• Paper: https://arxiv.org/abs/2401.01647
• Code: https://github.com/cgtuebingen/SIGNeRF

Single Mesh Diffusion Models with Field Latents for Texture Generation

• Paper: https://arxiv.org/abs/2312.09250
• Code: https://github.com/google-research/google-research/tree/master/mesh_diffusion

SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion

• Paper: https://arxiv.org/abs/2311.15855
• Code: https://github.com/SiTH-Diffusion/SiTH

SPAD: Spatially Aware Multiview Diffusers

• Paper: https://arxiv.org/abs/2402.05235
• Code: https://github.com/yashkant/spad

Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors

• Paper: https://arxiv.org/abs/2312.04963
• Code: https://github.com/BiDiff/bidiff

Text-to-3D using Gaussian Splatting

• Paper: https://arxiv.org/abs/2309.16585
• Code: https://github.com/gsgen3d/gsgen

The More You See in 2D, the More You Perceive in 3D

• Paper: https://arxiv.org/abs/2404.03652
• Code: https://github.com/sap3d/sap3d

Tiger: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process

• Paper: https://cvlab.cse.msu.edu/pdfs/Ren_Kim_Liu_Liu_TIGER_supp.pdf
• Code: https://github.com/Zhiyuan-R/Tiger-Diffusion

Towards Realistic Scene Generation with LiDAR Diffusion Models

• Paper: https://arxiv.org/abs/2404.00815
• Code: https://github.com/hancyran/LiDAR-Diffusion

UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusion

• Paper: https://arxiv.org/abs/2404.06851
• Code: https://github.com/weiqi-zhang/UDiFF

ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models

• Paper: https://arxiv.org/abs/2312.01305
• Code: https://github.com/ubc-vision/vivid123

6.3D编辑(3D Editing)

GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting

• Paper: https://arxiv.org/abs/2311.14521
• Code: https://github.com/buaacyw/GaussianEditor

GenN2N: Generative NeRF2NeRF Translation

• Paper: https://arxiv.org/abs/2404.02788
• Code: https://github.com/Lxiangyue/GenN2N

Makeup Prior Models for 3D Facial Makeup Estimation and Applications

• Paper: https://arxiv.org/abs/2403.17761
• Code: https://github.com/YangXingchao/makeup-priors

7.多模态大语言模型(Multi-Modal Large Language Models)

Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

• Paper: https://arxiv.org/abs/2312.03818
• Code: https://github.com/SunzeY/AlphaCLIP

Anchor-based Robust Finetuning of Vision-Language Models

• Paper: https://arxiv.org/abs/2404.06244
• Code: https://github.com/LixDemon/ARF

Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters

• Paper: https://arxiv.org/abs/2403.11549
• Code: https://github.com/JiazuoYu/MoE-Adapters4CL

Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction

• Paper: https://arxiv.org/abs/2403.18447
• Code: https://github.com/InhwanBae/LMTrajectory

Can’t make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models

• Paper: https://arxiv.org/abs/2405.20305
• Code:

Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

• Paper: https://arxiv.org/abs/2311.08046
• Code: https://github.com/PKU-YuanGroup/Chat-UniVi

Compositional Chain-of-Thought Prompting for Large Multimodal Models

• Paper: https://arxiv.org/abs/2311.17076
• Code: https://github.com/chancharikmitra/CCoT

Describing Differences in Image Sets with Natural Language

• Paper: https://arxiv.org/abs/2312.02974
• Code: https://github.com/Understanding-Visual-Datasets/VisDiff

Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models

• Paper: https://arxiv.org/abs/2403.17589
• Code: https://github.com/YBZh/DMN

Efficient Stitchable Task Adaptation

• Paper: https://arxiv.org/abs/2311.17352
• Code: https://github.com/ziplab/Stitched_LLaMA

Efficient Test-Time Adaptation of Vision-Language Models

• Paper: https://arxiv.org/abs/2403.18293
• Code: https://github.com/kdiAAA/TDA

Exploring the Transferability of Visual Prompting for Multimodal Large Language Models

• Paper: https://arxiv.org/abs/2404.11207
• Code: https://github.com/zycheiheihei/transferable-visual-prompting

FairCLIP: Harnessing Fairness in Vision-Language Learning

• Paper: https://arxiv.org/abs/2403.19949
• Code: https://github.com/Harvard-Ophthalmology-AI-Lab/FairCLIP

FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplication

• Paper: https://arxiv.org/abs/2404.16123
• Code:

FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models

• Paper: https://arxiv.org/abs/2404.16123
• Code:

Generative Multimodal Models are In-Context Learners

• Paper: https://arxiv.org/abs/2312.13286
• Code: https://github.com/baaivision/Emu/tree/main/Emu2

GLaMM: Pixel Grounding Large Multimodal Model

• Paper: https://arxiv.org/abs/2311.03356
• Code: https://github.com/mbzuai-oryx/groundingLMM

GPT4Point: A Unified Framework for Point-Language Understanding and Generation

• Paper: https://arxiv.org/abs/2312.02980
• Code: https://github.com/Pointcept/GPT4Point

InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

• Paper: https://arxiv.org/abs/2312.14238
• Code: https://github.com/OpenGVLab/InternVL

Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning

• Paper: https://arxiv.org/abs/2404.00909
• Code:

Let’s Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation

• Paper: https://arxiv.org/abs/2312.02439
• Code: https://github.com/sail-sg/CLoT

LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge

• Paper: https://arxiv.org/abs/2311.11860
• Code: https://github.com/rshaojimmy/JiuTian

LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning

• Paper: https://arxiv.org/abs/2311.18651
• Code: https://github.com/Open3DA/LL3DA

Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding

• Paper: https://arxiv.org/abs/2311.16922
• Code: https://github.com/DAMO-NLP-SG/VCD

MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

• Paper: https://arxiv.org/abs/2311.17049
• Code: https://github.com/apple/ml-mobileclip

MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric

• Paper: https://arxiv.org/abs/2403.07839
• Code:

Narrative Action Evaluation with Prompt-Guided Multimodal Interaction

• Paper: https://arxiv.org/abs/2404.14471
• Code: https://github.com/shiyi-zh0408/NAE_CVPR2024

OneLLM: One Framework to Align All Modalities with Language

• Paper: https://arxiv.org/abs/2312.03700
• Code: https://github.com/csuhan/OneLLM

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models

• Paper: https://arxiv.org/abs/2403.01849
• Code: https://github.com/TreeLLi/APT

OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

• Paper: https://arxiv.org/abs/2402.19479
• Code: https://github.com/shikiw/OPERA

Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

• Paper: https://arxiv.org/abs/2311.17911
• Code: https://github.com/snap-research/Panda-70M

PixelLM: Pixel Reasoning with Large Multimodal Model

• Paper: https://arxiv.org/abs/2312.02228
• Code: https://github.com/MaverickRen/PixelLM

PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization

• Paper: https://arxiv.org/abs/2404.09011
• Code:

Prompt Highlighter: Interactive Control for Multi-Modal LLMs

• Paper: https://arxiv.org/abs/2312.04302
• Code: https://github.com/dvlab-research/Prompt-Highlighter

PromptKD: Unsupervised Prompt Distillation for Vision-Language Models

• Paper: https://arxiv.org/abs/2403.02781
• Code: https://github.com/zhengli97/PromptKD

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

• Paper: https://arxiv.org/abs/2311.06783
• Code: https://github.com/Q-Future/Q-Instruct

SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models

• Paper: https://arxiv.org/abs/2403.13263
• Code: https://github.com/ivattyue/SC-Tune

SEED-Bench: Benchmarking Multimodal Large Language Models

• Paper: https://arxiv.org/abs/2311.17092
• Code: https://github.com/AILab-CVC/SEED-Bench

SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining

• Paper: https://arxiv.org/abs/2404.01156
• Code:

The Manga Whisperer: Automatically Generating Transcriptions for Comics

• Paper: https://arxiv.org/abs/2401.10224
• Code: https://github.com/ragavsachdeva/magi

UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All

• Paper: https://arxiv.org/abs/2403.12532
• Code:

VBench: Comprehensive Benchmark Suite for Video Generative Models

• Paper: https://arxiv.org/abs/2311.17982
• Code: https://github.com/Vchitect/VBench

VideoChat: Chat-Centric Video Understanding

• Paper: https://arxiv.org/abs/2305.06355
• Code: https://github.com/OpenGVLab/Ask-Anything

ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

• Paper: https://arxiv.org/abs/2312.00784
• Code: https://github.com/mu-cai/ViP-LLaVA

ViTamin: Designing Scalable Vision Models in the Vision-language Era

• Paper: https://arxiv.org/abs/2404.02132
• Code: https://github.com/Beckschen/ViTamin

ViT-Lens: Towards Omni-modal Representations

• Paper: https://github.com/TencentARC/ViT-Lens
• Code: https://arxiv.org/abs/2308.10185

8.其他任务(Others)

AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error

• Paper: https://arxiv.org/abs/2401.17879
• Code: https://github.com/jonasricker/aeroblade

Diff-BGM: A Diffusion Model for Video Background Music Generation

• Paper: https://openaccess.thecvf.com/content/CVPR2024/html/Li_Diff-BGM_A_Diffusion_Model_for_Video_Background_Music_Generation_CVPR_2024_paper.html
• Code: https://github.com/sizhelee/Diff-BGM

EvalCrafter: Benchmarking and Evaluating Large Video Generation Models

• Paper: https://arxiv.org/abs/2310.11440
• Code: https://github.com/evalcrafter/EvalCrafter

On the Content Bias in Fréchet Video Distance

• Paper: https://arxiv.org/abs/2404.12391
• Code: https://github.com/songweige/content-debiased-fvd

TexTile: A Differentiable Metric for Texture Tileability

• Paper: https://arxiv.org/abs/2403.12961v1
• Code: https://github.com/crp94/textile

持续更新~

参考

CVPR 2024 论文和开源项目合集(Papers with Code)

相关整理

• Awesome-AIGC-Research-Groups
• Awesome-Low-Level-Vision-Research-Groups
• Awesome-CVPR2024-CVPR2021-CVPR2020-Low-Level-Vision
• Awesome-ECCV2020-Low-Level-Vision