Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution

TL;DR

This paper introduces a mesh-based approach for extracting dense, long-range motion trajectories of articulated humans in videos, emphasizing spatial structure and mesh evolution for improved accuracy and consistency.

Contribution

It proposes a novel mesh evolution framework that considers spatial structure, detects self-occlusion, and iteratively adjusts mesh vertices for reliable long-range motion trajectories.

Findings

Achieves higher accuracy than state-of-the-art methods.

Demonstrates robustness in challenging sequences.

Ensures physically plausible mesh deformation.

Abstract

This letter presents a novel approach to extract reliable dense and long-range motion trajectories of articulated human in a video sequence. Compared with existing approaches that emphasize temporal consistency of each tracked point, we also consider the spatial structure of tracked points on the articulated human. We treat points as a set of vertices, and build a triangle mesh to join them in image space. The problem of extracting long-range motion trajectories is changed to the issue of consistency of mesh evolution over time. First, self-occlusion is detected by a novel mesh-based method and an adaptive motion estimation method is proposed to initialize mesh between successive frames. Furthermore, we propose an iterative algorithm to efficiently adjust vertices of mesh for a physically plausible deformation, which can meet the local rigidity of mesh and silhouette constraints. Finally, we compare the proposed method with the state-of-the-art methods on a set of challenging sequences. Evaluations demonstrate that our method achieves favorable performance in terms of both accuracy and integrity of extracted trajectories.