Reconstruction5 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. PROX-D, PROX-E, PROX-S 정리 Resolving 3D Human Pose Ambiguities with 3D Scene Constraints AbstractWe show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene. Our key contribution is to exploit static 3D scene structure to better estimate human pose from monocular images. The method enforces Proximal Relationships with Object eXclusion and is called PROX. To test this, we.. 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. 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