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
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