H4D: Human 4D Modeling by Learning Neural Compositional Representation - Technology Org

Modeling 3D human shape is important for many human-centric tasks, such as pose estimation and body shape fitting. However, further research is needed for applications involving dynamic signals, e. g. 3D moving humans. It combines a linear prior model with residual encoded in a learned auxiliary code. Each temporal sequence of 3D human shapes is encoded with compact latent codes, which then can be used to reconstruct the input sequence through a decoder. The additional auxiliary latent code compensates for the inaccurate motion and enriches the geometry details.