KBody: Towards general, robust, and aligned monocular whole-body estimation
- Nikolaos Zioulis
- James F. O'Brien
KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body's parameters. Acknowledging the importance of high quality correspondences, it leverages ``virtual joints" to improve fitting performance, disentangles the optimization between the pose and shape parameters, and integrates asymmetric distance fields to strike a balance in terms of pose and shape capturing capacity, as well as pixel alignment. We also show that generative model inversion offers a strong appearance prior that can be used to complete partial human images and used as a building block for generalized and robust monocular body fitting. Project page: https://klothed.github.io/KBody.
Nikolaos Zioulis and James F. O'Brien. "KBody: Towards general, robust, and aligned monocular whole-body estimation". In Proceedings of 1st Workshop on Reconstruction of Human-Object Interactions (RHOBIN) at CVPR 2023, page 11, June 2023.