Summary of Unipose: a Unified Multimodal Framework For Human Pose Comprehension, Generation and Editing, by Yiheng Li et al.
UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing
by Yiheng Li, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents UniPose, a framework that uses Large Language Models (LLMs) to understand, generate, and edit human poses across various modalities such as images, text, and 3D SMPL poses. The framework employs a pose tokenizer to convert 3D poses into discrete tokens, enabling integration with the LLM within a unified vocabulary. To enhance fine-grained pose perception, the paper proposes a mixture of visual encoders, including a pose-specific encoder. UniPose demonstrates competitive performance across various pose-relevant tasks and adapts to unseen tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UniPose is a new way for computers to understand and manipulate human poses. Currently, computers can only control one type of signal at a time, but UniPose lets them work with different types of signals like images, text, or 3D shapes. This helps in real-world scenarios where we need computers to understand and work with multiple kinds of information. The paper shows that UniPose is good at understanding and generating human poses, and it can even learn new things on its own. |
Keywords
» Artificial intelligence » Encoder » Tokenizer
