Peizhuo Li

Peizhuo Li

Direct Doctorate in Computer Science

IGL | ETH Zurich

Short Bio

My name is Peizhuo Li (李沛卓). I am a direct doctorate student at Interactive Geometry Lab under the supervision of Prof. Olga Sorkine-Hornung. My research interest lies in the intersection between deep learning and computer graphics. In particular, I am interested in practical problems related to character animation. Prior to my PhD study, I was an intern at Visual Computing and Learning lab at Peking University and advised by Prof. Baoquan Chen.

Interests

  • Computer Graphics
  • Character Animation
  • Deep Learning

Education

  • Direct Doctorate, 2021 ~ Present

    ETH Zurich

  • BSc in Computer Science, 2017 ~ 2021

    Turing Class, Peking University

Recent Publications

Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior

We introduce a neural motion synthesis approach that uses accessible pose data to generate plausible character motions by transferring …
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WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds

We introduce a novel approach to learn a common phase manifold from motion datasets across different characters, such as human and dog, …
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Neural Garment Dynamics via Manifold-Aware Transformers

Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of …
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Example-based Motion Synthesis via Generative Motion Matching

We present Generative Motion Matching (GenMM), a generative model that “mines” as many diverse motions as possible from a …
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MoDi: Unconditional Motion Synthesis from Diverse Data

The emergence of neural networks revolutionized motion synthesis, yet synthesizing diverse motions remains challenging. We present …
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GANimator: Neural Motion Synthesis from a Single Sequence

We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator …
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Learning Skeletal Articulations with Neural Blend Shapes

We develop a neural technique for articulating 3D characters using enveloping with a pre-defined skeletal structure, which is essential …
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Skeleton-Aware Networks for Deep Motion Retargeting

We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, …
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