Computer Vision and Machine Learning

Zhi Li (PhD student)

MSc Zhi Li

Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
E1 4 - 626
+49 681 9325 2143
+49 681 9325 2099

Personal Information


HULC: 3D HUman Motion Capture with Pose Manifold SampLing and Dense Contact Guidance
S. Shimada, V. Golyanik, Z. Li, P. Pérez, W. Xu and C. Theobalt
Computer Vision -- ECCV 2022, 2022
MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes
Z. Li, S. Shimada, B. Schiele, C. Theobalt and V. Golyanik
International Conference on 3D Vision, 2022
(arXiv: 2208.08439, Accepted/in press)
3D human motion capture from monocular RGB images respecting interactions of<br>a subject with complex and possibly deformable environments is a very<br>challenging, ill-posed and under-explored problem. Existing methods address it<br>only weakly and do not model possible surface deformations often occurring when<br>humans interact with scene surfaces. In contrast, this paper proposes<br>MoCapDeform, i.e., a new framework for monocular 3D human motion capture that<br>is the first to explicitly model non-rigid deformations of a 3D scene for<br>improved 3D human pose estimation and deformable environment reconstruction.<br>MoCapDeform accepts a monocular RGB video and a 3D scene mesh aligned in the<br>camera space. It first localises a subject in the input monocular video along<br>with dense contact labels using a new raycasting based strategy. Next, our<br>human-environment interaction constraints are leveraged to jointly optimise<br>global 3D human poses and non-rigid surface deformations. MoCapDeform achieves<br>superior accuracy than competing methods on several datasets, including our<br>newly recorded one with deforming background scenes.<br>
Monocular 3D Multi-Person Pose Estimation via Predicting Factorized Correction Factors
Y. Guo, L. Ma, Z. Li, X. Wang and F. Wang
Computer Vision and Image Understanding, Volume 213, 2021