SpatialGen - An open-source 3D scene generation model from GroupCore Technology
SpatialGen is an open-source 3D scene generation model from GroupCore Technology. Based on a diffusion model architecture, it supports generating spatiotemporally consistent multi-view images from text descriptions, reference images, and 3D spatial layouts, and can further...
What is SpatialGen?
SpatialGen is an open-source 3D scene generation model from Quncore Technology. Based on a diffusion model architecture, it supports the generation of spatiotemporally consistent multi-view images from text descriptions, reference images, and 3D spatial layouts. It can further generate 3D Gaussian scenes and render walkthrough videos. Leveraging massive amounts of indoor 3D scene data, the model generates visually realistic images with accurate spatial attributes and physical relationships of objects under different camera angles, allowing users to freely traverse scenes and enjoy an immersive experience. SpatialGen solves the spatial consistency problem of existing video generation models, providing a powerful tool for AI video creation.
Main functions of SpatialGen
- Multi-view image generationBased on text descriptions, reference images, and 3D spatial layouts, generate multi-view images with spatiotemporal consistency to ensure that the same object maintains accurate spatial attributes and physical relationships from different perspectives.
- 3D Gaussian scene generationFurthermore, the generated multi-view images are transformed into 3D Gaussian scenes, supporting the rendering of roaming videos and providing users with an immersive 3D spatial experience.
- Spatiotemporal consistency guaranteeIn the generated video, the shape and spatial relationship of objects remain stable and consistent across multiple frames, solving the spatial consistency problem commonly found in existing video generation models.
- Controllable generation of parametric layoutIt supports controllable generation based on parameterized layout, and in the future, it will be able to achieve richer structured scene information control to meet the specific needs of different users for scene generation.
SpatialGen's technical principles
- Multi-view diffusion modelSpatialGen is based on a diffusion model architecture. By sampling multiple camera views in 3D space, it transforms the 3D layout into 2D semantic maps and depth maps for the corresponding views. Combining text descriptions and reference images, it generates RGB images, semantic maps, and depth maps for each view based on the diffusion model.
- Large-scale high-quality datasetsRelying on the massive indoor 3D scene data of Qunhe Technology, the data provides rich materials for model training, making the generated images more visually realistic and the spatial relationships of objects more accurate.
- 3D Reconstruction AlgorithmThe system uses reconstruction algorithms to transform generated multi-view images into 3D Gaussian scenes, achieving the conversion from 2D images to 3D scenes and providing users with a richer interactive experience.
- Spatiotemporal consistency technologyThrough specific algorithms and technologies, the consistency of generated multi-view images in time and space is ensured, avoiding problems such as positional shifts and spatial logic confusion of objects between different frames, thereby improving the quality and usability of video generation.
SpatialGen's project address
- GitHub repositoryhttps://github.com/manycore-research/SpatialGen
- HuggingFace model library: https://huggingface.co/manycore-research/SpatialGen-1.0
Application scenarios of SpatialGen
- Interior design and decorationIt generates multiple interior design schemes based on user-input descriptions or floor plans, helping designers quickly present the effects and optimize the schemes, thus improving design efficiency.
- Virtual Reality (VR) and Augmented Reality (AR)Generate realistic 3D scenes for VR and AR applications, providing immersive experiences such as virtual exhibition halls and tourist attractions, and enhancing user interaction.
- Game developmentIt can quickly generate 3D scenes and environments in games, such as indoor scenes and city streets, accelerating the game development process, reducing development costs, and enriching game scenes.
- Robot Training and SimulationGenerate 3D scenes such as homes and industrial workshops for robot training, providing rich training data to improve robot adaptability and performance.
- Film and television production and animationSpatialGen can generate high-quality 3D scenes and animations, such as futuristic cities and ancient buildings, for use in film and television production and animation backgrounds, improving production efficiency and providing realistic visual effects.