Plausible and Diverse Human Hand Grasping Motion Generation

Techniques to grasp targeted objects in realistic and diverse ways find many applications in computer graphics, robotics and VR. This study generates diverse grasping motions while keeping plausible final grasps for human hands. We first build on a Transformer-based VAE to encode diverse reaching motions into a latent representation noted as GMF and then train an MLP-based cVAE to learn the grasping affordance of targeted objects. Finally, through learning a denoising process, we condition GMF with affordance to generate grasping motions for the targeted object. We identify improvements in our results, and will further address them in future work.