Awesome-ECCV2024-AIGC

A Collection of Papers and Codes for ECCV2024 AIGC

整理汇总下2024年ECCV AIGC相关的论文和代码，具体如下。

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知乎：https://zhuanlan.zhihu.com/p/706699484

参考或转载请注明出处

ECCV2024官网：https://eccv.ecva.net/

ECCV接收论文列表：

ECCV完整论文库：

开会时间：2024年9月29日-10月4日

论文接收公布时间：2024年

【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

AnyControl: Create Your Artwork with Versatile Control on Text-to-Image Generation

• Paper: https://arxiv.org/abs/2406.18958
• Code: https://github.com/open-mmlab/AnyControl

A Watermark-Conditioned Diffusion Model for IP Protection

• Paper:
• Code: https://github.com/rmin2000/WaDiff

BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion

• Paper: https://arxiv.org/abs/2404.04544
• Code: https://github.com/gwang-kim/BeyondScene

ComFusion: Personalized Subject Generation in Multiple Specific Scenes From Single Image

• Paper: https://arxiv.org/abs/2402.11849
• Code:

Data Augmentation for Saliency Prediction via Latent Diffusion

• Paper:
• Code: https://github.com/IVRL/AugSal

Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics

• Paper: https://arxiv.org/abs/2310.17316
• Code: https://github.com/EnVision-Research/Defect_Spectrum

DiffFAS: Face Anti-Spoofing via Generative Diffusion Models

• Paper:
• Code: https://github.com/murphytju/DiffFAS

DiffiT: Diffusion Vision Transformers for Image Generation

• Paper: https://arxiv.org/abs/2312.02139
• Code: https://github.com/NVlabs/DiffiT

Large-scale Reinforcement Learning for Diffusion Models

• Paper: https://arxiv.org/abs/2401.12244
• Code: https://github.com/pinterest/atg-research/tree/main/joint-rl-diffusion

MasterWeaver: Taming Editability and Identity for Personalized Text-to-Image Generation

• Paper: https://arxiv.org/abs/2405.05806
• Code: https://github.com/csyxwei/MasterWeaver

Memory-Efficient Fine-Tuning for Quantized Diffusion Model

• Paper:
• Code: https://github.com/ugonfor/TuneQDM

OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models

• Paper: https://arxiv.org/abs/2403.10983
• Code: https://github.com/kongzhecn/OMG

Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts

• Paper: https://arxiv.org/abs/2403.09176
• Code: https://github.com/byeongjun-park/Switch-DiT

2.图像编辑(Image Editing)

A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting

• Paper: https://arxiv.org/abs/2312.03594
• Code: https://github.com/open-mmlab/PowerPaint

BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion

• Paper: https://arxiv.org/abs/2403.06976
• Code: https://github.com/TencentARC/BrushNet

FlexiEdit: Frequency-Aware Latent Refinement for Enhanced Non-Rigid Editing

• Paper:
• Code: https://github.com/kookie12/FlexiEdit

StableDrag: Stable Dragging for Point-based Image Editing

• Paper: https://arxiv.org/abs/2403.04437
• Code:

TinyBeauty: Toward Tiny and High-quality Facial Makeup with Data Amplify Learning

• Paper: https://arxiv.org/abs/2403.15033
• Code: https://github.com/TinyBeauty/TinyBeauty

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

Audio-Synchronized Visual Animation

• Paper: https://arxiv.org/abs/2403.05659
• Code: https://github.com/lzhangbj/ASVA

Dyadic Interaction Modeling for Social Behavior Generation

• Paper: https://arxiv.org/abs/2403.09069
• Code: https://github.com/Boese0601/Dyadic-Interaction-Modeling

EDTalk: Efficient Disentanglement for Emotional Talking Head Synthesis

• Paper: https://arxiv.org/abs/2404.01647
• Code: https://github.com/tanshuai0219/EDTalk

FreeInit : Bridging Initialization Gap in Video Diffusion Models

• Paper: https://arxiv.org/abs/2312.07537
• Code: https://github.com/TianxingWu/FreeInit

MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model

• Paper: https://arxiv.org/abs/2405.20222
• Code: https://github.com/MyNiuuu/MOFA-Video

ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video

• Paper: https://arxiv.org/abs/2310.01324
• Code:

4.视频编辑(Video Editing)

Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation

• Paper: https://arxiv.org/abs/2403.13745
• Code: https://github.com/G-U-N/Be-Your-Outpainter

DragAnything: Motion Control for Anything using Entity Representation

• Paper: https://arxiv.org/abs/2403.07420
• Code: https://github.com/showlab/DragAnything

5.3D生成(3D Generation/3D Synthesis)

EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion

• Paper: https://arxiv.org/abs/2405.00915
• Code: https://github.com/ymxlzgy/echoscene

GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

• Paper: https://arxiv.org/abs/2405.00915
• Code: https://github.com/ibrahimethemhamamci/GenerateCT

GVGEN:Text-to-3D Generation with Volumetric Representation

• Paper:
• Code: https://github.com/SOTAMak1r/GVGEN

Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM

• Paper: https://arxiv.org/abs/2403.07487
• Code: https://github.com/steve-zeyu-zhang/MotionMamba

ParCo: Part-Coordinating Text-to-Motion Synthesis

• Paper: https://arxiv.org/abs/2403.18512
• Code: https://github.com/qrzou/ParCo

Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models

• Paper: https://arxiv.org/abs/2311.17050
• Code: https://github.com/Yzmblog/SurfD

6.3D编辑(3D Editing)

Gaussian Grouping: Segment and Edit Anything in 3D Scenes

• Paper: https://arxiv.org/abs/2312.00732
• Code: https://github.com/lkeab/gaussian-grouping

SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer

• Paper: https://arxiv.org/abs/2403.18512
• Code: https://github.com/JarrentWu1031/SC4D

Texture-GS: Disentangling the Geometry and Texture for 3D Gaussian Splatting Editing

• Paper: https://arxiv.org/abs/2403.10050
• Code: https://github.com/slothfulxtx/Texture-GS

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

An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models

• Paper: https://arxiv.org/abs/2403.06764
• Code: https://github.com/pkunlp-icler/FastV

ControlCap: Controllable Region-level Captioning

• Paper: https://arxiv.org/abs/2401.17910
• Code: https://github.com/callsys/ControlCap

DriveLM: Driving with Graph Visual Question Answering

• Paper: https://arxiv.org/abs/2312.14150
• Code: https://github.com/OpenDriveLab/DriveLM

Elysium: Exploring Object-level Perception in Videos via MLLM

• Paper: https://arxiv.org/abs/2403.16558
• Code: https://github.com/Hon-Wong/Elysium

Empowering Multimodal Large Language Model as a Powerful Data Generator

• Paper:
• Code: https://github.com/zhaohengyuan1/Genixer

GiT: Towards Generalist Vision Transformer through Universal Language Interface

• Paper: https://arxiv.org/abs/2403.09394
• Code: https://github.com/Haiyang-W/GiT

How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs

• Paper: https://arxiv.org/abs/2311.17600
• Code: https://github.com/UCSC-VLAA/vllm-safety-benchmark

Long-CLIP: Unlocking the Long-Text Capability of CLIP

• Paper: https://arxiv.org/abs/2403.15378
• Code: https://github.com/beichenzbc/Long-CLIP

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

• Paper: https://arxiv.org/abs/2403.14624
• Code: https://github.com/ZrrSkywalker/MathVerse

Merlin:Empowering Multimodal LLMs with Foresight Minds

• Paper: https://arxiv.org/abs/2312.00589
• Code: https://github.com/Ahnsun/merlin

Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs

• Paper: https://arxiv.org/abs/2403.11755
• Code: https://github.com/jmiemirza/Meta-Prompting

MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

• Paper: https://arxiv.org/abs/2403.14624
• Code: https://github.com/isXinLiu/MM-SafetyBench

PointLLM: Empowering Large Language Models to Understand Point Clouds

• Paper: https://arxiv.org/abs/2308.16911
• Code: https://github.com/OpenRobotLab/PointLLM

R2-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations

• Paper: https://arxiv.org/abs/2403.04924
• Code: https://github.com/lxa9867/r2bench

SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation

• Paper:
• Code: https://github.com/AI-Application-and-Integration-Lab/SAM4MLLM

ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

• Paper: https://arxiv.org/abs/2311.12793
• Code: https://github.com/ShareGPT4Omni/ShareGPT4V

ST-LLM: Large Language Models Are Effective Temporal Learners

• Paper: https://arxiv.org/abs/2404.00308
• Code: https://github.com/TencentARC/ST-LLM

TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias

• Paper: https://arxiv.org/abs/2404.00384
• Code: https://github.com/shjo-april/TTD

UniIR: Training and Benchmarking Universal Multimodal Information Retrievers

• Paper: https://arxiv.org/abs/2311.17136
• Code: https://github.com/TIGER-AI-Lab/UniIR

8.其他任务(Others)

持续更新~

参考

相关整理

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