I am a postdoctoral researcher in the Stanford Vision and Learning Lab and Stanford NeuroAILab , where I am advised by Jiajun Wu and Dan Yamins. I received my PhD at at the Georgia Institute of Technology advised by James Rehg.
My main research interests are in computer vision and machine learning. I am interested in studying how the relationship between 3D computer vision, self-supervision, and learning from video can lead to computer vision systems that can generalize to novel concepts and settings with limited data.
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 - poster
paper / code / project page / demo
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
ShapeClipper: Scalable 3D Shape Learning 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 - poster
arxiv / code / project page / video
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
Dense feature-level self-supervised learning from multiple camera views without
any category labels leads to representations that can generalize to novel categories.
Stefan Stojanov, Anh Thai, Zixuan Huang, James M. Rehg
NeurIPS 2022 - Poster
arxiv / code / project page / poster / video
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
A 3D-unsupervised model that learn shapes of multiple object categories at once.
Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James M. Rehg
ECCV 2022 - Poster
arxiv / 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 - Poster
The Benefits of Depth Information for Head-Mounted Gaze Estimation
Fusing depth and image information improves deep models' robustness
to fitment and slip for head mounted gaze estimation
Stefan Stojanov, Sachin Talathi, Abhishek Sharma
ETRA 2022 - Short Paper
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias
Learning representations to generalize based on 3D shape and then learning
to map images into them leads to improved low-shot generalization.
Stefan Stojanov, Anh Thai, James M. Rehg
CVPR 2021 - Poster
arxiv / code / dataset / project page
3D Reconstruction of Novel Object Shapes from Single Images
An implicit SDF representation-based method for single view 3D shape reconstruction
Anh Thai*, Stefan Stojanov*, James M. Rehg
3DV 2021 - Oral
arxiv / code / project page
Incremental Object Learning from Contiguous Views
Repetition of learned concepts in continual learning ameliorates catastrophic forgettng.
Stefan Stojanov, Anh Thai*, Samarth Mishra*, James M. Rehg
CVPR 2019 - Oral - Best paper award finalist
pdf / code / dataset / project page / video
Unsupervised 3D Pose Estimation with Geometric Self-supervision
Utilizing adversarial learning to estimate 3D human pose without 3D supervision.
Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal, Dylan Drover, Stefan Stojanov, James M. Rehg
CVPR 2019 - Poster