Body Representation
Body representation research focuses on creating accurate and efficient computational models of the human body for various applications, from improving sign language translation to enabling more realistic virtual try-ons and advanced robotics. Current research emphasizes developing robust and generalizable models, often employing neural networks (including graph neural networks and radiance fields) to capture complex body shapes, poses, and movements from various data sources (images, sensor readings, motion capture). These advancements are significant for improving human-computer interaction, accessibility technologies, and the development of more human-like robots capable of complex tasks and interactions.
Papers
GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning
Kento Kawaharazuka, Kei Okada, Masayuki Inaba
Restoration of Reduced Self-Efficacy Caused by Chronic Pain through Manipulated Sensory Discrepancy
Matti Itkonen, Riku Kawabata, Satsuki Yamauchi, Shotaro Okajima, Hitoshi Hirata, Shingo Shimoda