Seed3D 1.0 — Image to 3D Assets
A foundation model by ByteDance Seed that unites the scalability of generative modeling with the reliability of explicit physics simulation to produce diverse, high-quality 3D assets ready for physics simulation.
What is Seed3D 1.0
Seed3D 1.0 addresses three key challenges in 3D generation: high-fidelity asset generation, physics engine compatibility, and scalable scene composition. These capabilities take an important step toward enabling the world simulators that embodied AI requires.
Key Capabilities
High-Fidelity Geometry & Texture
Consistently outperforms baseline methods in geometry generation with higher ULIP-I and Uni3D-I scores. Strong capabilities in multi-view image and PBR materials generation, preserving fine surface details.
Simulation-Ready Assets
Produces watertight, manifold geometry that integrates directly into Isaac Sim. VLM estimates real-world scale, and default material properties enable immediate physics simulation without manual tuning.
Scalable Scene Generation
Extends from object to scene generation through a factorized approach. VLM extracts object instances and spatial relationships, enabling scenes from indoor offices to large-scale urban environments.
Performance Evaluation
Human Evaluation
Assessed across 43 input images along 6 key dimensions. Achieves highly competitive performance in all dimensions with strong fidelity in generating fine details and accurately reconstructing intricate features.
Geometry Generation
Consistently outperforms all baseline methods, achieving higher ULIP-I and Uni3D-I scores, indicating better alignment between generated geometry and input images.
Texture Generation
Strong capabilities in multi-view image and PBR materials generation. Reports results using ground-truth multi-view images to demonstrate PBR estimation ability.
Simulation-Ready Generation
Assets integrate directly into Isaac Sim for physics-based simulation and robotic manipulation testing. The physics engine provides real-time feedback on contact forces, object dynamics, and manipulation outcomes.
Scene Generation
Extends from object generation to scene generation through a factorized approach. A VLM extracts object instances and spatial relationships, then Seed3D synthesizes geometry and materials for each object. The final scene is assembled according to the predicted spatial layout — from indoor offices to large-scale urban scenes.
Use Cases
Embodied AI Training
Generate scalable training data for vision-language-action models through diverse manipulation scenarios.
Game & Content Creation
Rapidly generate high-quality 3D props and scenes for virtual environments and game development.
Robotics Simulation
Create realistic environments for testing robotic grasping, navigation, and multi-object interaction.
Explore Seed3D 1.0
Learn more about the Seed3D foundation model and try the official demo.