Stefan Stojanov


I am a PhD student in the School of Interactive Computing 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.

In the Summer of 2021 I was a research intern at Facebook Reality Labs. In the Fall of 2018 and Summer of 2019 I was a research intern at Amazon Lab 126.

Before Georgia Tech I did my bachelors at Bard College. I worked on Summer research projects with Michael Lawrence at the Broad Institute and with Sven Anderson at BSRI (Bard Summer Research Institute).



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

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

arxiv / code


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