MIDI
M
MIDI
Overview :
MIDI is an innovative image-to-3D scene generation technology that utilizes a multi-instance diffusion model to directly generate multiple 3D instances with accurate spatial relationships from a single image. The core of this technology lies in its multi-instance attention mechanism, which effectively captures inter-object interactions and spatial consistency without complex multi-step processing. MIDI excels in image-to-scene generation, suitable for synthetic data, real-world scene data, and stylized scene images generated by text-to-image diffusion models. Its main advantages include efficiency, high fidelity, and strong generalization ability.
Target Users :
This product primarily targets researchers and developers in computer vision, 3D modeling, and graphics, as well as industry professionals interested in generating 3D scenes from a single image. It provides an innovative solution for users who need efficient, high-quality 3D scene generation, suitable for academic research, content creation, virtual reality, and game development.
Total Visits: 10.8K
Top Region: US(43.72%)
Website Views : 65.7K
Use Cases
In academic research, researchers can use MIDI to generate 3D scenes for validating new algorithms or models.
In game development, developers can quickly generate 3D scenes from concept images to accelerate the construction of game worlds.
In virtual reality applications, MIDI can transform user-provided images into immersive 3D scenes, enhancing user experience.
Features
Generates multiple 3D instances from a single image, supporting direct scene composition.
Employs a multi-instance attention mechanism to capture inter-object interactions and spatial consistency.
Uses partial object images and global scene context as input to directly model object completion.
Supervises the interaction between 3D instances through limited scene-level data, while using single-object data for regularization.
Supports various data types, including synthetic data, real-world scene data, and stylized scene images.
The texture of the generated 3D scene can be further optimized using MV-Adapter.
Efficient training and generation process, with a total processing time of only 40 seconds.
The model code is open-source, facilitating use and extension by researchers and developers.
How to Use
1. Visit the MIDI project page to learn about its features and capabilities.
2. Download and install the relevant code libraries and dependencies.
3. Prepare the input image, which can be synthetic data, real-world scene images, or stylized images.
4. Use the MIDI model to process the input image and generate multiple 3D instances.
5. Combine the generated 3D instances into a complete 3D scene.
6. If necessary, use MV-Adapter to further optimize scene textures.
7. Post-process or apply the generated 3D scene as needed.
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