I'm a third-year PhD student at University of Toronto, advised by Dr. Babak Taati , and Faculty Affiliate Researcher at
Vector institute . My research focuses on
analyzing videos for human motion analysis including 3D human pose estimation, 3D human mesh recovery, action recognition, and gait assessment. I'm currently doing an internship in Pickford AI, focusing on video style transferring.
I'm interested in computer vision, Self-supervised learning, generative models, and their
application to a range of problems. Currently I'm focusing on 3D human pose/mesh estimation from
monocular videos. Some of my works are mentioned below.
LIFT enables unified implicit neural representations across diverse tasks by leveraging localized implicit functions and a hierarchical latent generator.
GAITGen is a generative framework that synthesizes realistic gait sequences conditioned on Parkinson’s severity. Using a Conditional Residual VQ-VAE and tailored Transformers, it disentangles motion and pathology features to produce clinically meaningful gait data. GAITGen enhances dataset diversity and improves performance in parkinsonian gait analysis tasks.
STARS enhances the Mask Autoencoder (MAE) approach in self-supervised learning by applying
contrastive tuning. We also show that MAE approaches fail in few-shot settings and achieve improved
performance by using the proposed method.