Seed3D - Image to 3D Model

Seed3D stands apart from traditional 3D generation models by producing simulation-ready assets with watertight manifold geometry that integrates directly into physics engines. While other image-to-3D models prioritize visual quality alone, Seed3D maintains the geometric precision and physical properties required for realistic physics simulation and robotic manipulation. This makes Seed3D the ideal choice for embodied AI researchers who need both visual fidelity and physics accuracy in their 3D assets.

Yes, absolutely. Seed3D-generated 3D models integrate seamlessly with industry-standard physics engines like Isaac Sim, Unity, and Unreal Engine. The Seed3D system automatically generates collision meshes and estimates real-world scale for each 3D object, enabling immediate deployment without any manual cleanup or configuration. This plug-and-play compatibility makes Seed3D perfect for researchers who want to quickly populate their simulation environments with diverse 3D assets.

Seed3D is designed for maximum flexibility in input requirements. At minimum, Seed3D can generate high-quality 3D models from just a single input image, making it incredibly accessible for users with limited data. For users seeking enhanced material quality, Seed3D can also leverage multi-view images to produce more accurate PBR material estimations. This versatility makes Seed3D suitable for a wide range of 3D generation workflows.

Seed3D employs a sophisticated decomposition-based approach to scene generation. First, vision-language models analyze input images to extract individual object instances and understand their spatial relationships. Then, Seed3D generates each object separately with complete geometry and materials. Finally, the Seed3D system intelligently assembles these 3D objects according to predicted spatial layouts, creating coherent and physically plausible complete scenes. This approach allows Seed3D to handle everything from simple object arrangements to complex multi-room environments.

Seed3D has been rigorously evaluated across multiple dimensions. In comprehensive user studies comparing 6 different 3D generation methods across 43 test images, Seed3D achieved top rankings in all 6 evaluation dimensions: clarity, input image fidelity, geometry quality, perspective accuracy, material quality, and detail richness. Technical benchmarks show Seed3D's superior ULIP-I and Uni3D-I scores, indicating better geometry-image alignment compared to existing 3D generation methods. These results demonstrate that Seed3D delivers both perceptual quality and technical excellence.

Seed3D is specifically designed to address the core challenges facing embodied AI researchers. The fundamental problem in embodied AI is generating massive amounts of diverse training data while maintaining the physics accuracy required for sim-to-real transfer. Seed3D solves this by enabling large-scale creation of diverse 3D manipulation scenarios with real-time physics feedback. Researchers can use Seed3D to generate thousands of unique 3D objects and scenes for training vision-language-action models, testing robotic policies, and building comprehensive evaluation benchmarks. With Seed3D, embodied AI development becomes dramatically more scalable and efficient.

Seed3D is optimized for efficient 3D generation without sacrificing quality. The generation speed varies depending on output complexity, but Seed3D can typically produce a complete 3D model from a single image in a matter of minutes on standard hardware. When deployed on Volcano Engine's optimized infrastructure, Seed3D's generation speed is further accelerated, making it practical for researchers who need to generate large batches of 3D assets for their embodied AI training pipelines.

Yes. While Seed3D generates high-quality 3D assets automatically, all outputs are provided in standard 3D formats that can be further edited using conventional 3D modeling tools. The watertight geometry produced by Seed3D provides an excellent starting point for customization, and the PBR materials can be adjusted to match specific requirements. This flexibility allows researchers to use Seed3D for rapid prototyping while maintaining the ability to fine-tune 3D assets for specialized applications.