Zixuan Huang


My name is Zixuan Huang (黄子煊, pronounced as Zi-Shwan Hwung), currently a fourth-year CS PhD student at University of Illinois Urbana-Champaign advised by James Rehg. I got my masters degree at University of Wisconsin-Madison, where I was fortunate to work with Professor Yin Li. I received my B.E. in the Special Class for Gifted Young from University of Science and Technology of China (USTC) in 2018. My main research focus is scalable and generalizable 3D shape reconstruction.

In the past few years, I have also worked with Chao-Yuan Wu and Justin Johnson at FAIR Labs (Meta AI), Varun Jampani and Yuanzhen Li at Google Research, as well as Hongsheng Li and Shuai Yi at Sensetime Research.



PointInfinity: Resolution-Invariant Point Diffusion Models

Point diffusion model that trains on low resolution point clouds, while generates faithful high resolution point clouds. Performance continuously improves as inference resolution increases.

Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg, Chao-Yuan Wu

CVPR 2024

paper / project page


ZeroShape: Regression-based Zero-shot Shape Reconstruction

SOTA 3D shape reconstructor with high computational efficiency and low training data budget.

Zixuan Huang*, Stefan Stojanov*, Anh Thai, Varun Jampani, James M. Rehg

CVPR 2024

paper / code / project page / demo


TripoSR: Fast 3D Object Reconstruction from a Single Image

Large open-source model for high-quality 3D reconstruction from a single image under one second.

Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, Yan-Pei Cao

Tech Report, 2024

paper / code / model / demo


ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-based Consistency

CLIP and geometric consistency constraints facilitate scalable learning of object shape reconstruction.

Zixuan Huang, Varun Jampani, Anh Thai, Yuanzhen Li, Stefan Stojanov, James M. Rehg

CVPR 2023

paper / code / project page / video


Low-shot Object Learning with Mutual Exclusivity Bias

Mutual Exclusivity Bias enables fast learning of objects that generalizes.

Anh Thai, Ahmad Humayun, Stefan Stojanov, Zixuan Huang, Bikram Boote, James M. Rehg

NeurIPS 2023, Datasets and Benchmarks Track

paper / code / project page


Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization

Multi-view self-supervised learning that allows for low-shot category recognition.

Stefan Stojanov, Anh Thai, Zixuan Huang, James M. Rehg

NeurIPS 2022

paper / code / project page / poster / video


Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues

A 3D-unsupervised model that learns shapes of multiple object categories at once.

Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James M. Rehg

ECCV 2022

paper / code / project page / poster / video


The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction

Continual learning of 3D shape reconstruction does not suffer from catastrophic
forgetting as much as discriminative learning tasks.

Anh Thai, Stefan Stojanov, Zixuan Huang, James M. Rehg

3DV 2022

paper / code


Interpretable and Accurate Fine-grained Recognition via Region Grouping

A model that recognizes objects in an interpretable way via region grouping and a part-occurrence prior.

Zixuan Huang, Yin Li

CVPR 2020 (oral presentation)

paper / code / project page / slides / video


HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion

A Multi-scale sparsity-invariant network for monocular depth completion.

Zixuan Huang, Junming Fan, Shenggan Cheng, Shuai Yi, Xiaogang Wang, Hongsheng Li