Hand Grasps Synthesis3 Grasping Field: Learning Implicit Representations for Human Grasp, 3DV’20 논문 링크 : https://arxiv.org/pdf/2008.04451.pdf AbstractYet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object;(3) it should interact with the object in a semantically and physically plausible manner.여전히, hum.. 2023. 4. 6. Hand-Object Contact Consistency Reasoning for Human Grasps Generation, ICCV’21 논문 링크 : https://openaccess.thecvf.com/content/ICCV2021/papers/Jiang_Hand-Object_Contact_Consistency_Reasoning_for_Human_Grasps_Generation_ICCV_2021_paper.pdf1. IntroductionIn this paper, we study the interactions via generation: As shown in Fig. 1, given only a 3D object in the world coordinate, we generate the 3D human hand for grasping it. We argue that it is critical for the hand contact poin.. 2023. 4. 6. GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes, CVPR’19 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2020/papers/Corona_GanHand_Predicting_Human_Grasp_Affordances_in_Multi-Object_Scenes_CVPR_2020_paper.pdf1. IntroductionIn order to predict feasible human grasps, we introduce GanHand, a multi-task GAN architecture that given solely one input image: 1) estimates the 3D shape/pose of the objects; 2) predicts the best grasp type according to a taxo.. 2023. 4. 6. 이전 1 다음