The Data Infrastructure for Physical AI
The world's leading AI researchers are building world models and spatial intelligence. They need high-fidelity 3D training data from real environments. DreamVu's omnidirectional capture platform delivers it at scale.
The PRISM Dataset
Our research paper demonstrates state-of-the-art performance in physical AI benchmarks.
The Perspective Problem
Humanoids need egocentric views (what they see) and exocentric views (how they appear to others). Traditional capture misses half the picture. DreamVu's synchronized dual-stream capture — Alia 360° exocentric + GoPro egocentric — gives you both simultaneously.
The Multimodal Gap
Physical AI models need vision + language + action data together. Most datasets provide vision only — leaving teams to stitch together incomplete signals. DreamVu delivers all three, synchronized.
The Sim-to-Real Gap
Humanoids trained in simulation fail when deployed in real environments. DreamVu captures the real world in formats that translate directly into Isaac Sim and back — closing the sim-to-real loop.
Physical AI Infrastructure
Sashi Reddi
Managing Partner at SRI Capital. Founder & former CEO of AppLabs (acquired by CSC). PhD Wharton, MS NYU, BTech IIT Delhi.
Rajat Aggarwal
BTech & Masters in CSE with specialization in Computational Photography from IIIT Hyderabad. His CVPR'16 paper on computational cameras became the seed for DreamVu.
Dr. Anoop Namboodiri
Professor at IIIT Hyderabad. 75+ published papers. Built systems currently deployed at massive scale.
Parikshit Sakurikar
PhD in Computational Photography from IIIT Hyderabad. Eight years focused on ML, high-performance computing for CV.