A Skeleton-Driven Neural Occupancy Representation for Articulated Hands
This work addresses the challenge of accurate and differentiable hand modeling for tasks like conditional generation, offering a novel representation that is incremental over existing parametric models.
The paper tackles the problem of representing articulated hands by introducing HALO, a skeleton-driven neural occupancy representation that bridges 3D keypoints and neural implicit surfaces, resulting in improved physical plausibility and user preference in generated hands.
We present Hand ArticuLated Occupancy (HALO), a novel representation of articulated hands that bridges the advantages of 3D keypoints and neural implicit surfaces and can be used in end-to-end trainable architectures. Unlike existing statistical parametric hand models (e.g.~MANO), HALO directly leverages 3D joint skeleton as input and produces a neural occupancy volume representing the posed hand surface. The key benefits of HALO are (1) it is driven by 3D key points, which have benefits in terms of accuracy and are easier to learn for neural networks than the latent hand-model parameters; (2) it provides a differentiable volumetric occupancy representation of the posed hand; (3) it can be trained end-to-end, allowing the formulation of losses on the hand surface that benefit the learning of 3D keypoints. We demonstrate the applicability of HALO to the task of conditional generation of hands that grasp 3D objects. The differentiable nature of HALO is shown to improve the quality of the synthesized hands both in terms of physical plausibility and user preference.