Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939103 | Pattern Recognition | 2018 | 69 Pages |
Abstract
In this paper, a novel end-to-end system for the fast reconstruction of human actor performances into 3D mesh sequences is proposed, using the input from a small set of consumer-grade RGB-Depth sensors. The proposed framework, by offline pre-reconstructing and employing a deformable actor's 3D model to constrain the on-line reconstruction process, implicitly tracks the human motion. Handling non-rigid deformation of the 3D surface and applying appropriate texture mapping, it finally produces a dynamic sequence of temporally-coherent textured meshes, enabling realistic Free Viewpoint Video (FVV). Given the noisy input from a small set of low-cost sensors, the focus is on the fast (“quick-post”), robust and fully-automatic performance reconstruction. Apart from integrating existing ideas into a complete end-to-end system, which is itself a challenging task, several novel technical advances contribute to the speed, robustness and fidelity of the system, including a layered approach for model-based pose tracking, the definition and use of sophisticated energy functions, parallelizable on the GPU, as well as a new texture mapping scheme. The experimental results on a large number of challenging sequences, and comparisons with model-based and model-free approaches, demonstrate the efficiency of the proposed approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Dimitrios S. Alexiadis, Nikolaos Zioulis, Dimitrios Zarpalas, Petros Daras,