Yuxuan Mu (Matthew)

Currently a Ph.D. student at GrUVi Lab, Simon Fraser University, working on 3D computer vision and animation. I primarily focus on 3D character motion modeling advised by Professor Xue bin (Jason) Peng.

I obtained my master’s degree at University of Alberta advised by Professor Li Cheng. I've worked as a research intern at Huawei Canada (Noah’s Ark Lab) on 3D generation and reconstruction with Juwei Lu and Xinxin Zuo.

Email  /  GitHub  /  LinkedIn  /  X

Research

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

'*' indicates equal contribution.

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

The first 3D diffusion model directly upon Gaussian 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