CVPR2024｜AIGC(图像生成，视频生成等)相关论文汇总（附论文链接/开源代码/解析）【持续更新】

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CVPR2024｜AIGC相关论文汇总（如果觉得有帮助，欢迎点赞和收藏）

Awesome-CVPR2024-AIGC 1.图像生成(Image Generation/Image Synthesis) ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations InstanceDiffusion: Instance-level Control for Image Generation Instruct-Imagen: Image Generation with Multi-modal Instruction MACE: Mass Concept Erasure in Diffusion Models PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models Residual Denoising Diffusion Models 2.图像编辑(Image Editing) PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models 3.视频生成(Video Generation/Image Synthesis) Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners 4.视频编辑(Video Editing) 5.3D生成(3D Generation/3D Synthesis) EscherNet: A Generative Model for Scalable View Synthesis 6.其他多任务(Others) InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models 参考相关整理

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完整论文列表：

开会时间：2024年6月17日-6月21日

论文接收公布时间：

【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.其他多任务(Others)

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

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

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

MACE: Mass Concept Erasure in Diffusion Models

Paper: Code: https://github.com/Shilin-LU/MACE

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

Residual Denoising Diffusion Models

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

2.图像编辑(Image Editing)

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

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

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

4.视频编辑(Video Editing)

5.3D生成(3D Generation/3D Synthesis)

EscherNet: A Generative Model for Scalable View Synthesis

Paper: https://arxiv.org/abs/2402.03908 Code: https://github.com/kxhit/EscherNet

6.其他多任务(Others)

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

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
持续更新~

参考

CVPR 2024 论文和开源项目合集(Papers with Code)