MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes
원문 초록
3D human motion capture from monocular RGB images respecting interactions of a subject with complex and possibly deformable environments is a very challenging, ill-posed and under-explored problem. Existing methods address it only weakly and do not model possible surface deformations often occurring when humans interact with scene surfaces. In contrast, this paper proposes MoCapDeform, i.e., a new framework for monocular 3D human motion capture that is the first to explicitly model non-rigid deformations of a 3D scene for improved 3D human pose estimation and deformable environment reconstruction. MoCapDeform accepts a monocular RGB video and a 3D scene mesh aligned in the camera space. It first localises a subject in the input monocular video along with dense contact labels using a new raycasting based strategy. Next, our human-environment interaction constraints are leveraged to jointly optimise global 3D human poses and non-rigid surface deformations. MoCapDeform achieves superior accuracy than competing methods on several datasets, including our newly recorded one with deforming background scenes.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation
HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose Estimation
3D Human Pose Estimation via Spatial Graph Order Attention and Temporal Body Aware Transformer
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
토론
이 논문과 연결된 포럼 글이 아직 없습니다.