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 lies at the intersection of deep learning and computer graphics, with an emphasis on modeling, control, and generative models for character animation, as well as related problems in geometry and physics. Prior to my PhD study, I was a student researcher 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 …
Symposium on Computer Animation (SCA) 2024, Computer Graphics Forum
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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, …
SIGGRAPH 2024, Technical Papers Track
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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 …
Eurographics 2024, Computer Graphics Forum
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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 …
SIGGRAPH 2023, ACM Transactions on Graphics (TOG)
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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 …
CVPR 2023
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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 …
SIGGRAPH 2022, ACM Transactions on Graphics (TOG)
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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 …
SIGGRAPH 2021, ACM Transactions on Graphics (TOG)
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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, …
SIGGRAPH 2020, ACM Transactions on Graphics (TOG)
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