Capture Everything.

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.

1,000
Hours of 3D Data
500+
Distinct Skills
Research Breakthrough

The PRISM Dataset

Our research paper demonstrates state-of-the-art performance in physical AI benchmarks.

66.6%
Overall error rate reduction
+23.8%
Average accuracy gain (62.8% → 86.6%)
Embodied Reasoning error reduction (45.5% → 9.1%)
270K
Training samples
11.8M
Video frames
The Race to Build World Models
Starts with Real-World Data
The most influential minds in AI have converged on one conclusion: the next frontier isn't larger language models — it's machines that understand and interact with the physical world.

Spatial Intelligence Is the Next Frontier

Fei-Fei Li — the Stanford professor who created ImageNet and catalyzed the deep learning revolution — has made spatial intelligence the focus of her latest company, World Labs. Her thesis: AI must learn to perceive, reason about, and act in three-dimensional space. Not from text. Not from flat images. From spatially rich, real-world data.

"Spatial intelligence is the next major capability AI needs to develop. It's how humans and animals make sense of the world — and it's what's missing from today's AI systems."

— Fei-Fei Li, Stanford HAI & World Labs

Yann LeCun — Meta's Chief AI Scientist and Turing Award winner — has been equally direct. He argues that the path to truly intelligent machines runs through world models: internal representations of how the physical world works, learned from observation, not text.

"A system trained on text will never understand the physical world. You need world models — learned from video and sensory data — that can predict what happens next."

— Yann LeCun, Meta AI & NYU

Both visions share a common prerequisite: massive amounts of high-fidelity, spatially aware, real-world 3D data. And that's exactly what doesn't exist today — at least, not at the scale or quality these models demand.

World Models Need Real Worlds

VLA (Vision-Language-Action) models can't learn physics, spatial relationships, or manipulation skills from 2D images and text. They need dense 3D captures of real environments with real people performing real tasks.

The Data Bottleneck Is Critical

Billions have been poured into model architectures — GR00T, RT-2, Octo, π₀ — but the training data barely exists. Open-source robotics datasets are small, narrow-FOV, and lack the 3D spatial richness these models require.

DreamVu Fills the Gap

Our dual-stream capture system — Alia 360° exocentric + GoPro egocentric — produces exactly the data that world models and spatial AI systems need. Synchronized omnidirectional capture with depth + RGB at scale.

Why Training Physical AI Is So Hard
Humanoid robots don't just navigate — they manipulate objects, coordinate limbs, understand context, and learn from watching others. Current data falls short.

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.

From Breakthrough Research to
Physical AI Infrastructure
DreamVu began with breakthrough research in computational imaging at IIIT Hyderabad — a new optical design for capturing 360° stereoscopic video in a single shot, published at CVPR. Eight years of production deployment later, we're now building the data infrastructure the humanoid robotics industry needs.
SR

Sashi Reddi

Co-Founder & Chairman

Managing Partner at SRI Capital. Founder & former CEO of AppLabs (acquired by CSC). PhD Wharton, MS NYU, BTech IIT Delhi.

RA

Rajat Aggarwal

Co-Founder & CEO

BTech & Masters in CSE with specialization in Computational Photography from IIIT Hyderabad. His CVPR'16 paper on computational cameras became the seed for DreamVu.

AN

Dr. Anoop Namboodiri

Co-Founder & Chief Science Officer

Professor at IIIT Hyderabad. 75+ published papers. Built systems currently deployed at massive scale.

PS

Parikshit Sakurikar

Co-Founder & VP Imaging & AI

PhD in Computational Photography from IIIT Hyderabad. Eight years focused on ML, high-performance computing for CV.

SRI Capital
Ben Franklin Technology Partners
Broad Street Angels
Philadelphia, PA
US Headquarters
Hyderabad, India
R&D Center
Three Ways to Partner with DreamVu
Building the Physical AI ecosystem together — multiple paths to collaborate, from capture to deployment.
📹

Data Partner

Earn $50/hr capturing data with our Alia Starter Kit ($10K). 2-3 month payback.

How It Works

1. Purchase Alia Starter Kit
2. Complete Onboarding
3. Capture & Earn
Apply as Data Partner
🏢

Venue Partner

Provide access to your facility for DreamVu capture teams. Earn revenue from your space.

Ideal For

Retail stores, warehouses, manufacturing floors, kitchens — any real-world environment where robots will operate.

DreamVu deploys capture rigs on a recurring schedule. You earn a venue access fee while contributing to robotics.

Become a Venue Partner
🤖

Technology Partner

Integrate DreamVu data into your robotics or AI platform. Access our API and custom data pipelines.

Benefits

Priority access to new datasets, custom format delivery, and dedicated integration support for robotics companies, simulation platforms, and AI labs.

Explore Technology Partnership