Stochastic Whole-Body Grasping with Contact


Overview. Whole-body grasping motion sequences (in beige) generated by SAGA starting from a given pose (in white) to approach and grasp randomly placed unseen objects. For each sample, we present the hand and finger motions in the last few frames on the left column.


Human grasping synthesis has numerous applications including AR/VR, video games and robotics. While some methods have been proposed to generate realistic hand-object interaction for object grasping and manipulation, they typically only consider the hand interacting with objects. In this work, our goal is to synthesize whole-body grasping motion. Given a 3D object, we aim to generate diverse and natural whole-body human motions that approach and grasp the object. 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) 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 ending poses and human-object contacts. (b) Grasping motion infilling. Given an initial pose and the generated whole-body grasping pose as the starting and ending poses 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 being the first generative framework to synthesize realistic and expressive whole-body motions that approach and grasp randomly placed unseen objects.



Paper    Code   


SAGA: Stochastic Whole-Body Grasping with Contact
Yan Wu*, Jiahao Wang*, Yan Zhang, Siwei Zhang, Otmar Hilliges, Fisher Yu, Siyu Tang

   title = {SAGA: Stochastic Whole-Body Grasping with Contact},
   author = {Wu, Yan and Wang, Jiahao and Zhang, Yan and Zhang, Siwei and Hilliges, Otmar and Yu, Fisher and Tang, Siyu},
   booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
   year = {2022}


Yan Wu Jiahao Wang Yan Zhang Siwei Zhang Otmar Hilliges Fisher Yu Siyu Tang


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