Summary of Flexpose: Pose Distribution Adaptation with Limited Guidance, by Zixiao Wang et al.
FlexPose: Pose Distribution Adaptation with Limited Guidance
by Zixiao Wang, Junwu Weng, Mengyuan Liu, Bei Yu
First submitted to arxiv on: 18 Dec 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 This paper proposes a novel method for calibrating pre-trained pose generators using pose distribution priors. The authors observe that different pose datasets share similar pose hinge-structure priors with varying geometric transformations, which enables them to adapt a pre-trained generator to new pose distributions with only limited annotation guidance. They treat human pose joint coordinates as skeleton images and transfer a pre-trained pose annotation generator by fine-tuning its linear layers to closely relate to the pose transformation. The proposed method, FlexPose, achieves state-of-the-art performance on several cross-dataset settings, outperforming existing generative-model-based transfer learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in computer vision where annotating human poses for new images is time-consuming and expensive. They found that different datasets have similar patterns in the way humans move, but with small differences. They developed a method to adjust their pose generator model so it can create accurate poses for new images using only a few examples of what the correct poses should look like. The method is called FlexPose and works by fine-tuning some key parts of the model. This means that FlexPose can be used on different datasets and still produce good results. |
Keywords
» Artificial intelligence » Fine tuning » Generative model » Transfer learning
