Awesome-CVPR2025-AIGC

A Collection of Papers and Codes for CVPR2025 AIGC

整理汇总下2025年CVPR AIGC相关的论文和代码，具体如下。

【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)

2025年CVPR可复现论文合集，含代码https://docs.qq.com/doc/DQ25HbWt6WmdOZEta?u=7f01826fa3f140bb8e36e875087997e8&nlc=1

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

Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient

Paper: https://arxiv.org/abs/2411.17787

Code: https://github.com/czg1225/CoDe

Paper: https://arxiv.org/abs/2408.16266

Code: https://github.com/scuwyh2000/Diff-II

Paper: https://arxiv.org/abs/2412.15119

Code: https://github.com/Epiphqny/PAR

Paper: https://arxiv.org/abs/2412.03177

Code: https://github.com/hqhQAQ/PatchDPO

Paper: https://arxiv.org/abs/2501.01423

Code: https://github.com/hustvl/LightningDiT

Paper: https://arxiv.org/abs/2410.18737

Code: https://github.com/thuxmf/recfg

Paper: https://arxiv.org/abs/2403.09055

Code: https://github.com/ironjr/semantic-draw

Paper: https://arxiv.org/abs/2412.04852

Code: https://github.com/taco-group/SleeperMark

Paper: https://arxiv.org/abs/2412.03069

Code: https://github.com/ByteFlow-AI/TokenFlow

2.图像编辑(Image Editing)

Attention Distillation: A Unified Approach to Visual Characteristics Transfer

• Paper: https://arxiv.org/abs/2502.20235

• Code: https://github.com/xugao97/AttentionDistillation

Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing

• Paper: https://arxiv.org/abs/2411.16832

• Code: https://github.com/taco-group/FaceLock

EmoEdit: Evoking Emotions through Image Manipulation

• Paper: https://arxiv.org/abs/2405.12661

• Code: https://github.com/JingyuanYY/EmoEdit

K-LoRA: Unlocking Training-Free Fusion of Any Subject and Style LoRAs

• Paper: https://arxiv.org/abs/2502.18461

• Code: https://github.com/HVision-NKU/K-LoRA

StyleStudio: Text-Driven Style Transfer with Selective Control of Style Elements

• Paper: https://arxiv.org/abs/2412.08503

• Code: https://github.com/Westlake-AGI-Lab/StyleStudio

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

ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way

• Paper: https://arxiv.org/abs/2410.06241

• Code: https://github.com/Bujiazi/ByTheWay

Identity-Preserving Text-to-Video Generation by Frequency Decomposition

• Paper: https://arxiv.org/abs/2411.17440

• Code: https://github.com/PKU-YuanGroup/ConsisID

InstanceCap: Improving Text-to-Video Generation via Instance-aware Structured Caption

• Paper: https://arxiv.org/abs/2412.09283

• Code: https://github.com/NJU-PCALab/InstanceCap

WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model

• Paper: https://arxiv.org/abs/2411.17459

• Code: https://github.com/PKU-YuanGroup/WF-VAE

4.视频编辑(Video Editing)

Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models

• Paper: https://arxiv.org/abs/2407.15642

• Code: https://github.com/maxin-cn/Cinemo

Generative Inbetweening through Frame-wise Conditions-Driven Video Generation

• Paper: https://arxiv.org/abs/2412.11755

• Code: https://github.com/Tian-one/FCVG

X-Dyna: Expressive Dynamic Human Image Animation

• Paper: https://arxiv.org/abs/2501.10021

• Code: https://github.com/bytedance/X-Dyna

5.3D生成(3D Generation/3D Synthesis)

Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation

• Paper: https://arxiv.org/abs/2411.16185

• Code: https://github.com/YuQiao0303/Fancy123

Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass

• Paper: https://arxiv.org/abs/2501.13928

• Code: https://github.com/facebookresearch/fast3r

GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation

• Paper: https://arxiv.org/abs/2406.06526

• Code: https://github.com/hzxie/GaussianCity

LT3SD: Latent Trees for 3D Scene Diffusion

• Paper: https://arxiv.org/abs/2409.08215

• Code: https://github.com/quan-meng/lt3sd

Towards High-fidelity 3D Talking Avatar with Personalized Dynamic Texture

• Paper: https://arxiv.org/abs/2503.00495

• Code: https://github.com/XuanchenLi/TexTalk

You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale

• Paper: https://arxiv.org/abs/2412.06699

• Code: https://github.com/baaivision/See3D

• 

6.3D编辑(3D Editing)

DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters

• Paper: https://arxiv.org/abs/2411.17423

• Code: https://github.com/yisuanwang/DRiVE

FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video

• Paper: https://arxiv.org/abs/2411.15604

• Code: https://github.com/zjwfufu/FateAvatar

Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters

• Paper: https://arxiv.org/abs/2411.18197

• Code: https://github.com/jasongzy/Make-It-Animatable

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

Automated Generation of Challenging Multiple Choice Questions for Vision Language Model Evaluation

• Paper: https://arxiv.org/abs/2501.03225

• Code: https://github.com/yuhui-zh15/AutoConverter

RAP-MLLM: Retrieval-Augmented Personalization for Multimodal Large Language Model

• Paper: https://arxiv.org/abs/2410.13360

• Code: https://github.com/Hoar012/RAP-MLLM

SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model

• Paper: https://arxiv.org/abs/2412.01550

• Code: https://github.com/hq-King/SeqAfford

ShowUI: One Vision-Language-Action Model for GUI Visual Agent

• Paper: https://arxiv.org/abs/2411.17465

• Code: https://github.com/showlab/ShowUI

8.其他任务(Others)

Continuous and Locomotive Crowd Behavior Generation

• Paper:

• Code: https://github.com/InhwanBae/Crowd-Behavior-Generation

Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

• Paper: https://arxiv.org/abs/2412.15322

• Code: https://github.com/hkchengrex/MMAudio