Computer Vision
We work at the intersection of geometry, perception, and learning, designing models that reconstruct the world from images and generate it back out. Architectures we build respect the structure of their inputs, with spatial priors drawn from simulation.
- Reconstruction from sparse observations, including Gaussian-splat flames and plant geometry from images
- Generative models (diffusion and transformers) conditioned on simulated or structured priors
Publications
- Auditing Metrics for Synthetic Data Utility: A Cross-Domain Study of Appearance and Geometry WACV 2027
- MaSC: A Masked Similarity Metric for Evaluating Concept-Driven Generation NeurIPS 2026
- Gaussians on Fire: High-Frequency Reconstruction of Flames ECCV 2026
- SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition ECCV 2026 Workshops
- Neural Surface Priors for Editable Gaussian Splatting IJCNN 2025