• 关注领域：

• AIGC
  

• 3D computer vision learning

• Fine-grained learning

• GNN

• 其他
  

• 声明
  

• 论文较多，时间有限，本专栏无法做文章的讲解，只挑选出符合PaperABC研究兴趣和当前热点问题相关的论文，如果你的research topic和上述内容有关，那本专栏可作为你的论文更新源或Paper reading list．

Paper list:

今日ArXiv共更新108篇

03.28共更新248篇

03.27共更新128篇

NeRF

HandNeRF: Neural Radiance Fields for Animatable Interacting Hands

https://arxiv.org/pdf/2303.13825.pdf

手势仿真NeRF.

ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field

https://arxiv.org/pdf/2303.13817.pdf

南洋理工S-Lab的工作．

GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images

https://arxiv.org/pdf/2303.13777.pdf

UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields

https://arxiv.org/pdf/2303.14167.pdf

整体思想还是做组合物体生成，但是这篇文章聚焦urban scene.　手速真快啊！

AIGC

MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion

https://arxiv.org/pdf/2303.14139.pdf

基于脑信号来绘制图像的文章，前段时间非常火．

Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation

https://arxiv.org/pdf/2303.13873.pdf

解耦几何和外观的文本到3D的生成工作，这个领域近半年已经卷成红海了！

CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout

https://arxiv.org/pdf/2303.13843.pdf

又是一篇，组合多物体生成．来自于HKUST. 你们卷吧　我不玩了．

Conditional Image-to-Video Generation with Latent Flow Diffusion Models

https://arxiv.org/pdf/2303.13744.pdf

利用latent flow diffusion model 实现图像到视频的生成任务．

Anti-DreamBooth: Protecting users from personalized text-to-image synthesis

https://arxiv.org/pdf/2303.15433.pdf

一篇防止生成模型类似于DreamBooth，滥用图像的工作．

Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation

https://arxiv.org/pdf/2303.15413.pdf

真的是什么样的idea都有，卷不动这个领域．

Text-to-Image Diffusion Models are Zero-Shot Classifiers

https://arxiv.org/pdf/2303.15233.pdf

扩散模型学习到的知识能用来分类吗？很有启发性的工作．

Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior

https://arxiv.org/pdf/2303.14184.pdf

单张图像生成3D物体，感觉做法和Magic3D很类似．

VLP

Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data

https://arxiv.org/pdf/2303.14080.pdf

表格数据和图像数据的多模态预训练工作．

Accelerating Vision-Language Pretraining with Free Language Modeling

https://arxiv.org/pdf/2303.14038.pdf

VLP领域做加速的工作，来自腾讯团队．

Prompt Tuning based Adapter for Vision-Language Model Adaption

https://arxiv.org/pdf/2303.15234.pdf

利用prompt tuning来微调VLP大模型