CVPR2025.md

Awesome-CVPR2025-AIGC

A Collection of Papers and Codes for CVPR2025 AIGC

整理汇总下今年CVPR AIGC相关的论文和代码，具体如下。

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相关整理

• Awesome-ECCV2024-AIGC
• Awesome-AIGC-Research-Groups
• Awesome-Low-Level-Vision-Research-Groups
• Awesome-CVPR2024-CVPR2021-CVPR2020-Low-Level-Vision
• Awesome-ECCV2020-Low-Level-Vision

参考或转载请注明出处

CVPR2025官网：https://cvpr.thecvf.com/Conferences/2025

CVPR接收论文列表：

CVPR完整论文库：

开会时间：2025月6月11日-2025月6月15日

论文接收公布时间：2025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)

CacheQuant: Comprehensively Accelerated Diffusion Models

• Paper: https://arxiv.org/abs/2503.01323
• Code: https://github.com/BienLuky/CacheQuant

Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient

• Paper: https://arxiv.org/abs/2411.17787
• Code: https://github.com/czg1225/CoDe

DiC: Rethinking Conv3x3 Designs in Diffusion Models

• Paper: https://arxiv.org/abs/2501.00603
• Code: https://github.com/YuchuanTian/DiC

DreamText: High Fidelity Scene Text Synthesis

• Paper: https://arxiv.org/abs/2405.14701
• Code: https://github.com/CodeGoat24/DreamText

Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold Network

• Paper: https://arxiv.org/abs/2502.19754
• Code: https://github.com/PerceptionComputingLab/LDSB

Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models

• Paper:
• Code: https://github.com/sccsok/CoprGuard

Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification

• Paper: https://arxiv.org/abs/2408.16266
• Code: https://github.com/scuwyh2000/Diff-II

Parallelized Autoregressive Visual Generation

• Paper: https://arxiv.org/abs/2412.15119
• Code: https://github.com/Epiphqny/PAR

PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation

• Paper: https://arxiv.org/abs/2412.03177
• Code: https://github.com/hqhQAQ/PatchDPO

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

• Paper: https://arxiv.org/abs/2501.01423
• Code: https://github.com/hustvl/LightningDiT

Rectified Diffusion Guidance for Conditional Generation

• Paper: https://arxiv.org/abs/2410.18737
• Code: https://github.com/thuxmf/recfg

SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models

• Paper: https://arxiv.org/abs/2403.09055
• Code: https://github.com/ironjr/semantic-draw

SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models

• Paper: https://arxiv.org/abs/2412.04852
• Code: https://github.com/taco-group/SleeperMark

TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation

• Paper: https://arxiv.org/abs/2412.03069
• Code: https://github.com/ByteFlow-AI/TokenFlow

2.图像编辑(Image Editing)

AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models

• Paper: https://arxiv.org/abs/2412.04146
• Code: https://github.com/Crayon-Shinchan/AnyDressing

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

Recognition-Synergistic Scene Text Editing

• Paper: https://arxiv.org/abs/2502.18461
• Code: https://github.com/ZhengyaoFang/RS-STE

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

PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation

• Paper: https://arxiv.org/abs/2412.00596
• Code: https://github.com/pittisl/PhyT2V

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

FADE: Frequency-Aware Diffusion Model Factorization for Video Editing

• Paper:
• Code: https://github.com/EternalEvan/FADE

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

持续更新~