TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos

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Hauptverfasser: Wang, Yufu, Wang, Ziyun, Liu, Lingjie, Daniilidis, Kostas
Format: Preprint
Veröffentlicht: 2024
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author Wang, Yufu
Wang, Ziyun
Liu, Lingjie
Daniilidis, Kostas
author_facet Wang, Yufu
Wang, Ziyun
Liu, Lingjie
Daniilidis, Kostas
contents We propose TRAM, a two-stage method to reconstruct a human's global trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the presence of dynamic humans and uses the scene background to derive the motion scale. Using the recovered camera as a metric-scale reference frame, we introduce a video transformer model (VIMO) to regress the kinematic body motion of a human. By composing the two motions, we achieve accurate recovery of 3D humans in the world space, reducing global motion errors by a large margin from prior work. https://yufu-wang.github.io/tram4d/
format Preprint
id arxiv_https___arxiv_org_abs_2403_17346
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
Wang, Yufu
Wang, Ziyun
Liu, Lingjie
Daniilidis, Kostas
Computer Vision and Pattern Recognition
We propose TRAM, a two-stage method to reconstruct a human's global trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the presence of dynamic humans and uses the scene background to derive the motion scale. Using the recovered camera as a metric-scale reference frame, we introduce a video transformer model (VIMO) to regress the kinematic body motion of a human. By composing the two motions, we achieve accurate recovery of 3D humans in the world space, reducing global motion errors by a large margin from prior work. https://yufu-wang.github.io/tram4d/
title TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2403.17346