Yuxuan Mu (Matthew)

Currently a graduate student at University of Alberta, Vision and Learning Lab, working on 3D computer vision. I primarily focus on 3D human motion modeling advised by Prof. Li Cheng. I am going to join GrUVi Lab at Simon Fraser University as a Ph.D. student advised by Prof. Jason Peng.

Starting from March 2023, I also worked on 3D reconstruction and generation with Dr. Juwei Lu and Dr. Xinxin Zuo at Noah’s Ark Lab, Huawei Canada, as a research intern. My works are published/accepted in ICLR'24 and ECCV'24.

Email  /  GitHub  /  LinkedIn  /  X

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Research

My works involve 3D human motion estimation, generation, physics simulation and rendering, aiming to eventually augment our reality by building digital dynamic world clone.

'*' indicates equal contribution.

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GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction


Yuxuan Mu, Xinxin Zuo, Chuan Guo, Yilin Wang, Juwei Lu, Xiaofei Wu, Songcen Xu, Peng Dai, Youliang Yan, Li Cheng
Accepted for ECCV, 2024
webpage / paper /

3D Diffusion model upon the emerging Gausssian Splatting representation to tackle the single‐view real‐world object reconstruction, with fine-grained yet efficient conditioning, and generative modeling.

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MoMask: Generative Masked Modeling of 3D Human Motions


Chuan Guo*, Yuxuan Mu*, Muhammad Gohar Javed*, Sen Wang, Li Cheng
CVPR, 2024
webpage / paper / code / demo / Star

We introduce MoMask, a novel masked modeling frame work for text‐driven 3D human motion generation with a hierarchical quantization scheme.

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Generative Human Motion Stylization in Latent Space


Chuan Guo*, Yuxuan Mu*, Xinxin Zuo, Peng Dai, Youliang Yan, Juwei Lu, Li Cheng
ICLR, 2024
webpage / paper / code /

We propose a flexible motion style extraction and injection method from a generative perspective to solve the motion stylization task with probabilistic style space.

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RACon: Retrieval-Augmented Simulated Character Locomotion Control


Yuxuan Mu, Shihao Zou, Kangning Yin, Zheng Tian, Li Cheng, Weinan Zhang, Jun Wang
ICME (Oral), 2024
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We introduce an end-to-end hierarchical reinforcement learning method utilizes a task-oriented learnable retriever, a motion controller and a retrieval-augmented discriminator.

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Event‐based Human Pose Tracking by Spiking Spatiotemporal Transformer


Shihao Zou, Yuxuan Mu, Xinxin Zuo, Sen Wang, Li Cheng
Arxiv Preprint, 2023
paper / code /

Our SNNs approach uses at most 19.1% of the computation and 3.6% of the energy costs consumed by the existing methods while achieves superior performance.





Design and source code from Leonid Keselman's website