Summary of Smpler: Taming Transformers For Monocular 3d Human Shape and Pose Estimation, by Xiangyu Xu et al.
SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation
by Xiangyu Xu, Lijuan Liu, Shuicheng Yan
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 Transformer framework, SMPLer, is proposed for monocular 3D human shape and pose estimation. The framework addresses the limitation of existing Transformers, which have a quadratic computation and memory complexity with respect to feature length, by incorporating decoupled attention and an SMPL-based target representation. These designs enable effective utilization of high-resolution features in the Transformer. Additionally, novel modules such as multi-scale attention and joint-aware attention are introduced to further improve reconstruction performance. Experimental results demonstrate the effectiveness of SMPLer against existing methods both quantitatively and qualitatively, achieving an MPJPE of 45.2 mm on the Human3.6M dataset, outperforming Mesh Graphormer by over 10% with fewer parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SMPLer is a new way to estimate human shape and pose from just one camera view. This method uses Transformers, which are good at processing sequential data, but it makes them more efficient for large features. The approach also includes some special modules that help the model focus on important details. The results show that SMPLer works better than other methods, both in terms of accuracy and computational cost. |
Keywords
» Artificial intelligence » Attention » Pose estimation » Transformer
