We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Camera Motion Agnostic Method for Estimating 3D Human Poses.
- Authors
Kim, Seong Hyun; Jeong, Sunwon; Park, Sungbum; Chang, Ju Yong
- Abstract
Although the performance of 3D human pose and shape estimation methods has improved considerably in recent years, existing approaches typically generate 3D poses defined in a camera or human-centered coordinate system. This makes it difficult to estimate a person's pure pose and motion in a world coordinate system for a video captured using a moving camera. To address this issue, this paper presents a camera motion agnostic approach for predicting 3D human pose and mesh defined in the world coordinate system. The core idea of the proposed approach is to estimate the difference between two adjacent global poses (i.e., global motion) that is invariant to selecting the coordinate system, instead of the global pose coupled to the camera motion. To this end, we propose a network based on bidirectional gated recurrent units (GRUs) that predicts the global motion sequence from the local pose sequence consisting of relative rotations of joints called global motion regressor (GMR). We use 3DPW and synthetic datasets, which are constructed in a moving-camera environment, for evaluation. We conduct extensive experiments and prove the effectiveness of the proposed method empirically.
- Subjects
POSE estimation (Computer vision); CAMERAS; RANGE of motion of joints; CRANES (Birds); HUMAN beings
- Publication
Sensors (14248220), 2022, Vol 22, Issue 20, pN.PAG
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22207975