Published by Video Runtime · Source check 2026-09-14 · Evidence method · Suggest a correction
EDITORIALLY VERIFIED PRODUCT INFORMATION
About Generated Reality
Generate egocentric video worlds controlled by tracked hand poses and camera motion from VR equipment. Encodes per-frame 3D hand joints and camera trajectories, then injects those controls into a Wan2.2 video diffusion transformer.
Key features
Provides a concrete human-centric control path for dexterous hand-object interaction rather than camera navigation alone.
Getting started
Install the DiffSynth-Studio-based environment, prepare reference images plus tracked hand and camera controls, download the documented Wan2.2 checkpoint, and start with the supplied inference configuration.
Real-time interaction
Encodes per-frame 3D hand joints and camera trajectories, then injects those controls into a Wan2.2 video diffusion transformer.
Pricing
MIT code; self-hosted compute costs extra Repository code is published under MIT. Confirm separate model, dataset and dependency terms before reuse. No hosted price is inferred. Budget for the documented GPU, storage and setup requirements.
Limitations
The workflow depends on tracked 3D hand poses, camera calibration and large Wan2.2 checkpoints; it is a research pipeline rather than a hosted application.
EDITORIAL VIEW
Editor's Verdict
Generated Reality is included as a source-available implementation relevant to real-time generative video or interactive world systems.
Run the supplied example on representative hardware and review every linked licence before production use.
How It Works
- Encodes per-frame 3D hand joints and camera trajectories, then injects those controls into a Wan2.2 video diffusion transformer.
What We Like
- Provides a concrete human-centric control path for dexterous hand-object interaction rather than camera navigation alone.
Current Limitations
- The workflow depends on tracked 3D hand poses, camera calibration and large Wan2.2 checkpoints; it is a research pipeline rather than a hosted application.
Pricing & Access
- Repository code is published under MIT. Confirm separate model, dataset and dependency terms before reuse.
- No hosted price is inferred. Budget for the documented GPU, storage and setup requirements.
Verification Summary
- Official repository and licence reviewed on 2026-09-14. Video Runtime has not installed or benchmarked this project.
- Performance and compatibility statements below are attributed to the maintainers and are not site measurements.
Technical notes & official performance
Official Performance Data
- No single comparable real-time FPS or end-to-end latency claim was found in the inspected README; interactive suitability depends on the selected model and hardware.
Technical Notes
- The 14B workflow separates high-noise and low-noise diffusion transformers and can run inference across multiple GPUs.
Sources
- Official GitHub repository ↗github · accessed 2026-09-14
- Code licence ↗github · accessed 2026-09-14
Access, stage & mechanism
These are separate source-checked facts, not a live uptime monitor. Unverified fields are left open; transport and persistent sessions do not establish how frames are generated.
- Release stage
- Not verified
- Access mode
- Not verified
- Source availability
- Not verified
- Transport
- Not verified
- Interaction
- Not verified
- Session mode
- Not verified
- Generation mechanism
- Not verified
- Continuity method
- Not verified
- Licence status
- Not verified
- Tasks
- Not classified
CONTINUE EXPLORING
Related projects & models
Selected by research links and category, not by popularity.
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