Yuxuan Mu (Matthew)

I am 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.

Starting from March 2023, I worked on common 3D object reconstruction and generation with Dr. Juwei Lu and Dr. Xinxin Zuo at Noah’s Ark Lab, Huawei Canada, as a research intern.

Email  /  GitHub  /  DBLP  /  LinkedIn

Research

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

'*' indicates equal contribution.

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.

3DG²: 3D‐Gaussian Diffusion for Single‐View 3D Reconstruction

Diffusion model upon the emerging 3D Gausssian representation to tackle the single‐view real‐world object reconstruction, with efficient and faithful rendering, and generative modeling.

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