Mesh12 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. Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction, CVPR’20 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2020/papers/Hasson_Leveraging_Photometric_Consistency_Over_Time_for_Sparsely_Supervised_Hand-Object_Reconstruction_CVPR_2020_paper.pdfAbstractCollecting 3D ground-truth data for hand-object interactions is costly, tedious, and error-prone. To overcome this challenge we present a method to leverage photometric consistency across time when annotat.. 2023. 7. 21. Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild, ECCV’20 논문 링크 : https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570035.pdfAbstractWe present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment. Notably, our method runs on datasets without any scene- or object level 3D supervision. Our key insight is that co.. 2023. 7. 21. 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. 이전 1 2 3 다음