Survey of 3D Human Body Pose and Shape Estimation Methods for Contemporary Dance Applications
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Abstract (arXiv)
3D human body shape and pose estimation from RGB images is a challenging problem with potential applications in augmented/virtual reality, healthcare and fitness technology and virtual retail. Recent solutions have focused on three types of inputs: i) single images, ii) multi-view images and iii) videos. In this study, we surveyed and compared 3D body shape and pose estimation methods for contemporary dance and performing arts, with a special focus on human body pose and dressing, camera viewpoint, illumination conditions and background conditions. We demonstrated that multi-frame methods, such as PHALP, provide better results than single-frame method for pose estimation when dancers are performing contemporary dances.
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References · 8
- 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,612
- 2017End-to-end Recovery of Human Shape and Posecited by 885
- 2019Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loopcited by 627
- 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 620
- 2021PARE: Part Attention Regressor for 3D Human Body Estimationcited by 337
- 2020STAR: Sparse Trained Articulated Human Body Regressorcited by 194
- 2020Monocular Expressive Body Regression through Body-Driven Attentioncited by 155
- 2021Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimationcited by 30
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