We propose BodyFusion, a novel real-time geometry fusion method that can track and reconstruct non-rigid surface motion of a human performance using a single consumer-grade depth camera. To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method...
Authors
Tao Yu
Kaiwen Guo
Feng Xu
Yuan Dong
Zhaoqi Su
Jianhui Zhao
Qionghai Dai
Yebin Liu
Related Content
Learning Semantic Feature Map for Visual Content Recognition
The spatial relationship among objects provide rich clues to object contexts for visual recognition. In this paper, we propose to...
A Factorization Approach for Enabling Structure-From-Motion/SLAM Using Integer...
SLAM and SfM algorithms typically involve minimization of a cost-function by non-linear least-squares methods. The matrices involved are typically very...
Decoder Network over Lightweight Reconstructed Feature for Fast...
Recently, the community of style transfer is trying to incorporate semantic information into traditional system. This practice achieves better perceptual...
Colored Point Cloud Registration Revisited
We present an algorithm for aligning two colored point clouds. The key idea is to optimize a joint photometric and...