The synthesis of human grasping has numerous applications including AR/VR,
video games and robotics. While methods have been proposed to generate
realistic hand-object interaction for object grasping and manipulation, these
typically only consider interacting hand alone. Our goal is to synthesize
whole-body grasping motions. Starting from an arbitrary initial pose, we aim to
generate diverse and natural whole-body human motions to approach and grasp a
target object in 3D space. This task is challenging as it requires modeling
both whole-body dynamics and dexterous finger movements. To this end, we
propose SAGA (StochAstic whole-body Grasping with contAct), a framework which
consists of two key components: (a) Static whole-body grasping pose generation.
Specifically, we propose a multi-task generative model, to jointly learn static
whole-body grasping poses and human-object contacts. (b) Grasping motion
infilling. Given an initial pose and the generated whole-body grasping pose as
the start and end of the motion respectively, we design a novel contact-aware
generative motion infilling module to generate a diverse set of grasp-oriented
motions. We demonstrate the effectiveness of our method, which is a novel
generative framework to synthesize realistic and expressive whole-body motions
that approach and grasp randomly placed unseen objects. Code and models are
available at this https URL

提出了一种名为 SAGA 的新型的人体抓握生成框架，该框架以多任务生成模型为核心，实现了整体抓握姿态和人 - 物接触点的联合学习，并设计了接触感知的生成运动插值模块来生成多样的姿态运动。