Summary of Survey Of 3d Human Body Pose and Shape Estimation Methods For Contemporary Dance Applications, by Darshan Venkatrayappa et al.
Survey of 3D Human Body Pose and Shape Estimation Methods for Contemporary Dance Applications
by Darshan Venkatrayappa, Alain Tremeau, Damien Muselet, Philippe Colantoni
First submitted to arxiv on: 4 Jan 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 study focuses on estimating 3D human body shape and pose from RGB images, with applications in augmented/virtual reality, healthcare, and fitness technology. Researchers compared various methods using different types of inputs: single images, multi-view images, and videos. The survey highlights the importance of considering factors like camera viewpoint, illumination conditions, and background conditions for accurate 3D body shape and pose estimation. Notably, multi-frame methods like PHALP outperform single-frame methods in estimating poses when dancers perform contemporary dances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve our ability to estimate the 3D shape and movement of the human body from photos or videos. The study looks at different ways to do this, using various types of images or footage. It shows that some methods are better than others for certain types of dance movements. This is important because it could help with things like creating realistic avatars in video games or analyzing dancer movements. |
Keywords
» Artificial intelligence » Pose estimation
