Give Stable Virtual Camera a few photos of a scene and a camera path, and it renders what the scene would look like from viewpoints nobody photographed. It is a research release from Stability AI with the University of Oxford and UC Berkeley, and it is one of the more accessible ways to experiment with novel view synthesis without building a 3D reconstruction pipeline first.
| Repository | Stability-AI/stable-virtual-camera |
| Maintainer | Stability AI |
| Primary language | Python |
| GitHub stars | 1,663 (as of 15 September 2026) |
| Last push | 3 March 2026 |
| License status | Stability AI Non-Commercial License |
What it does
The README calls Stable Virtual Camera (SEVA) a “generalist diffusion model for Novel View Synthesis”. It takes any number of input views plus target camera positions and generates 3D-consistent views of the scene from those positions. Two checkpoints are published on Hugging Face, both 1.3B parameters at 576p:
- v1.0 — the initial release (March 2025).
- v1.1 — released June 2025, fixing cases where foreground objects came loose from the background.
The repository also includes a benchmark folder with the scenes, splits and input/target views used in the accompanying paper.
Who it’s for
- Researchers benchmarking novel view synthesis methods.
- 3D and VFX hobbyists who want to try camera moves around a still scene; the Gradio demo is aimed at non-experts.
Getting started
The README’s install is a recursive git clone followed by pip install -e ., with Python 3.10+ and PyTorch 2.6.0+. Windows users are told to use WSL, because flash attention isn’t supported on native Windows. The weights are gated: you log in with huggingface-cli login and request access on the model card before the code can download them. Then choose a demo: python demo_gr.py for the Gradio interface, or python demo.py for the command line. The README does not list a minimum GPU.
License status
Checked October 17, 2026: The Stability AI Non-Commercial License Agreement permits research and non-commercial uses subject to its conditions, including distribution and attribution requirements. Do not assume it permits commercial deployment. Read the model card and agreement for the treatment of outputs and derivatives.
Limitations and open questions
- No official training script; the README points to community discussion and a pending pull request instead.
- Gated weights and no native Windows support add setup friction.
- Output resolution for both checkpoints is 576p.
- We have not run the model; nothing here is a quality assessment.
Sources
- GitHub repository and README
- License file
- Paper: Stable Virtual Camera: Generative View Synthesis with Diffusion Models (arXiv 2503.14489)
- Project page
More research code with a generative angle: AI repositories · Repos.
This profile was drafted with AI assistance from the project’s public repository and reviewed by The Signal’s editors before publication. See our AI Disclosure.
