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.

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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.

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

The first 3D diffusion model directly upon Gausssian Splatting for real‐world object reconstruction, with fine-grained view-guided conditioning.

MoMask: Generative Masked Modeling of 3D Human Motions

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

Generative Human Motion Stylization in Latent Space

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

RACon: Retrieval-Augmented Simulated Character Locomotion Control

We introduce an end-to-end hierarchical reinforcement learning method utilizes a task-oriented learnable retriever, a motion controller and a retrieval-augmented discriminator.

Event‐based Human Pose Tracking by Spiking Spatiotemporal Transformer

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