Siyou Lin

Email: linsy21@mails.tsinghua.edu.cn

I am currently a PhD student at the Department of Automation, Tsinghua University, China, supervised by Prof. Yebin Liu. My current research is focused on data-driven 3D human avatars and cloth animation. My research involves different subfields in CV & CG, including geometry processing, rendering, animation and deep learning. I am also actively investigating generative models.

Prior to this, I received my Bachelor's degree in Mathematics from the Department of Mathematics, Tsinghua University. I was also a member of the Tsinghua Xuetang Mathematics Talent Program. During my undergraduate studies, I worked closely with Prof. Zuoqiang Shi from Yau Mathematical Sciences Center, Tsinghua University and Prof. Bin Wang from School of Software, Tsinghua University, on the problem of point cloud surface reconstruction.

Other than my educational experiences, I also worked on monocular 3D depth estimation in a remote internship at the GRASP lab, UPenn, supervised by Prof. Jianbo Shi from 2020 to 2021. I also spent time at Meta Zurich as a research scientist intern working on realistic dynamic clothing animation in 2024.

We present Winding Number Normal Consistency (WNNC) and an iterative algorithm that estimates globally consistent normal vectors. Our CUDA-based treecode-accelerated algorithm handles a million of points in half a minute.

We present LayGA, a layered Gaussian avatar model based on Animatable Gaussians, with improved geometry and the ability of animatable clothing transfer.

We leverage intrinsic manifold properties and neural deformation fields and propose a coarse-to-fine two-stage method for non-rigid garment alignment, achieving wrinkle-level and texture-level alignment.

We present CaPhy, a novel method combining 3D-supervised training with unsupervised physics-based losses for reconstructing animatable human avatars. Caphy allows estimating the physical properties of garments and producing realistic dynamic clothing.

We present CloSET, a point-based human avatar modeling method that allows loose clothing modeling by explicit template decomposition. We also release a real-world capture dataset of clothed humans in various poses.

We propose a First-Implicit-Then-Explicit (FITE) framework that combines the merits of both implicit and explicit representations for modeling human avatars in clothing.

We present Parametric Gauss Reconstruction (PGR), a method that reconstructs surfaces from point clouds without normals by parametrizing the Gauss formula.

*Work done while I was an undergraduate at the Department of Mathematical Sciences, Tsinghua University.

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