CARE-PD is the largest publicly available archive of 3D mesh gait data for Parkinson’s Disease, collected across 9 cohorts from 8 clinical centers. It provides standardized, anonymized SMPL representations and benchmark protocols for clinical motion analysis on PD.

LIFT uses meta-learning to efficiently encode diverse signals (images, 3D shapes, and more) into multiscale latent representations for downstream tasks like generation and classification.

Paper: arxiv.org/abs/2503.15420
Project page: amirhossein-kz.github.io/lift/

🙋 I'll be at ICCV and interested in meeting

📜 Abstract: arxiv.org/abs/2506.10036
🌐 Project page: github.com/TaatiTeam/To...

Great collaboration with Javad Rajabi, Soroush Mehraban, Seyedmorteza Sadat.

TPG outperforms SDXL, PAG, and SEG in unconditional generation, and closely matches CFG in conditional tasks across all metrics.

TPG mirrors CFG by producing guidance vectors nearly orthogonal to ground-truth noise and maintaining strong guidance throughout denoising. Unlike PAG/SEG, it avoids negative alignment and weak updates, leading to more effective, high-quality generation.

TPG, like CFG, effectively recovers global structure and coarse details during early denoising steps. This stage is crucial for image quality and prompt alignment, as it establishes the foundation for structure and semantics.

In conditional generation, TPG produces better results and matches prompts more accurately than other attention-based methods like PAG and SEG. It also behaves more like CFG in how its guidance works and how often it applies.

TPG is a novel, training-free approach that boosts diffusion model performance by perturbing (shuffling) intermediate token representations. It improves both conditional and unconditional generation while also strengthening prompt alignment.

Developed with Dr. Nimish Mittal, Dr. Amol Deshpande & Andrea Sabo at Kite Research Institute | Toronto Rehab - UHN.

The HAT app is approved by Health Canada and, for now, is only available in Canada. We will release it in the US after receiving FDA approval.

The app guides users through 9 steps to return the Beighton Score, making hypermobility testing and EDS screening accessible to providers and the public.

We did this back in 2023. I’d be curious to see how newer models perform if you get a chance to test them. The dataset is public. We had the first 15 seasons at the time, but I’m sure newer seasons are available now as well.