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Stable Virtual Camera

1.3B parameter image to video model for generating 3D consistent new scene views

#Image to video
#Transformer model
#3D scene generation
#New view synthesis
#non-commercial model
Stable Virtual Camera

Product Details

Stable Virtual Camera is a 1.3B parameter universal diffusion model developed by Stability AI, which is a Transformer image to video model. Its importance lies in providing technical support for New View Synthesis (NVS), which can generate 3D consistent new scene views based on the input view and target camera. The main advantages are the freedom to specify target camera trajectories, the ability to generate samples with large viewing angle changes and temporal smoothness, the ability to maintain high consistency without additional Neural Radiation Field (NeRF) distillation, and the ability to generate high-quality seamless loop videos of up to half a minute. This model is free for research and non-commercial use only, and is positioned to provide innovative image-to-video solutions for researchers and non-commercial creators.

Main Features

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- **New View Synthesis**: Generate 3D consistent new scene views based on input multiple views and target cameras, providing more perspective choices for scene creation.
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- **Free trajectory setting**: Allows users to freely specify target camera trajectories across a larger spatial range to meet diverse creative needs.
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- **Large viewing angle change generation**: Can generate samples with large viewing angle changes, enriching the display effect of video content and bringing a novel visual experience to the audience.
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- **Temporal Smoothing**: The generated samples are smooth in time, making the video transition natural and providing a better viewing experience.
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- **Simplified synthesis process**: High consistency can be maintained without additional NeRF distillation, simplifying the view synthesis process and improving creative efficiency.
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- **High-quality long video generation**: Able to generate high-quality, half-minute long videos with seamless looping characteristics, suitable for a variety of creative scenarios.
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- **Art Creation Support**: Can be used for the generation of artworks, as well as providing materials and creative inspiration in design and other artistic creation processes.
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- **Education and Research Assistance**: Provide technical support for educational or creative tools, and also help researchers study reconstruction models and explore the capabilities of the models.

How to Use

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1. Visit the project's GitHub repository to obtain the relevant code and documentation for using the model.
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2. Prepare the environment required to run the model, including installing the necessary dependencies, according to the instructions on GitHub.
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3. Collect the input view data used to generate the new view, ensuring that the data conforms to the format required by the model.
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4. According to the creative needs, determine the target camera trajectory and clarify the perspective and movement path of the new view you want to generate.
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5. Set the input view data and target camera trajectory information according to the input specifications of the model.
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6. Run the code to use the model to generate new scene views and videos.
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7. Analyze and adjust based on the generated results. If you are not satisfied, you can modify the input data or camera trajectory and run the model again until the desired effect is achieved.

Target Users

The target audience is mainly researchers, artists, designers, and educators. For researchers, this model can be used for research on new view synthesis, reconstruction models, etc., to help explore the performance and limitations of the model; artists and designers can use it to generate unique scene views and creative materials, enriching the content and visual effects of their works; educators can apply it in teaching tools to display knowledge in a more vivid way and improve teaching effects.

Examples

1. Researchers use this model to study the effect of view synthesis in different scenarios. By adjusting the target camera trajectory, they analyze the performance of the new views generated by the model in terms of 3D consistency.

2. When an artist creates digital paintings, he uses the scene views from different perspectives generated by Stable Virtual Camera to get inspiration and create works of art with unique perspectives.

3. When teachers make teaching videos about building structures, they use this model to generate 3D views of the building from different angles to help students understand the building structure more intuitively.

Quick Access

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Categories

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› AI model
› video generation

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