Hand-Object Interaction7 ContactOpt: Optimizing Contact to Improve Grasps, CVPR’21 논문 링크 : https://openaccess.thecvf.com/content/CVPR2021/papers/Grady_ContactOpt_Optimizing_Contact_To_Improve_Grasps_CVPR_2021_paper.pdfAbstractGiven a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes. Then, ContactOpt efficiently optimizes the pose of the hand to achieve desirable contact using a differenti.. 2023. 7. 21. 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. 이전 1 2 다음