HumanAI18 HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation, CVPR’20 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2020/papers/Doosti_HOPE-Net_A_Graph-Based_Model_for_Hand-Object_Pose_Estimation_CVPR_2020_paper.pdfAbstractHand-Object pose estimation(HOPE)는 손과 들고 있는 물체의 포즈를 동시에 감지하는 것을 목표로 한다. 본 논문에서는 2D와 3D에서 실시간으로 손과 물체의 포즈를 추정하는 HOPE-Net이라는 lightweight model을 제안한다. 여기에서는 두 가지의 adaptive graph convolution 을 사용하는데, 하나는 hand joint와 object corner의 2D 좌표를, 하나는 2.. 2023. 4. 6. Learning joint reconstruction of hands and manipulated objects, CVPR’19 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2019/papers/Hasson_Learning_Joint_Reconstruction_of_Hands_and_Manipulated_Objects_CVPR_2019_paper.pdfAbstractWe present an end to-end learnable model that exploits a novel contact loss that favors physically plausible hand-object constellations. Our approach improves grasp quality metrics over baselines, using RGB images as input. To train and e.. 2023. 4. 6. 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. 이전 1 2 3 4 5 다음