Summary of Genhmr: Generative Human Mesh Recovery, by Muhammad Usama Saleem et al.
GenHMR: Generative Human Mesh Recovery
by Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Pu Wang, Hongfei Xue, Srijan Das, Chen Chen
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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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 A novel generative framework called GenHMR is introduced for human mesh recovery (HMR) from monocular images. Unlike previous deterministic methods, which output a single prediction, GenHMR explicitly models and mitigates uncertainties in the 2D-to-3D mapping process by reformulating HMR as an image-conditioned generative task. The framework consists of two key components: a pose tokenizer to convert 3D human poses into latent tokens and an image-conditional masked transformer to learn conditional distributions of pose tokens. By sampling from these distributions, GenHMR iteratively decodes high-confidence pose tokens and refines the reconstruction using a 2D pose-guided technique. Experimental results on benchmark datasets demonstrate that GenHMR outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GenHMR is a new way to recover human meshes from single images. Usually, computers use deterministic methods to do this, but they can’t handle uncertainties in the data. GenHMR is different because it uses a generative approach to model these uncertainties. It has two main parts: one that converts 3D poses into tokens and another that learns how to generate these tokens based on an image prompt. By repeating this process, GenHMR can create multiple possible meshes and refine them using the original image. This results in more accurate mesh recovery than previous methods. |
Keywords
» Artificial intelligence » Prompt » Tokenizer » Transformer
