What is Seed3D 1.0
Seed3D 1.0 is a 3D generation large model launched by ByteDance's Seed team. It can generate high-precision 3D models from a single image, with high-fidelity asset generation, physics engine compatibility, and scalable scene composition capabilities. Based on an innovative Diffusion Transformer architecture and trained on large-scale data, it can generate fine geometric structures, realistic textures, and physically based rendering (PBR) materials. In performance evaluations, Seed3D 1.0 performs excellently in geometry generation and texture generation, with particularly obvious advantages in detail preservation and complex feature reconstruction. The generated 3D models can be directly integrated into physics simulation environments for robot operation testing, etc., and can be extended from single-object generation to complete scene generation, providing strong support for world simulators of embodied intelligence.
Main Features of Seed3D 1.0
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High-fidelity asset generation: It can generate fine geometric structures, realistic textures, and physically based rendering (PBR) materials from a single image, and the generated 3D models have high precision and high quality.
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Physics engine compatibility: The generated 3D models can be directly integrated into industry-standard physics simulation environments (such as Isaac Sim) for physics simulation and robot operation testing, supporting real-time physics simulation.
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Scalable scene composition capability: Through a decomposed generation method, it can extend from single-object generation to complete scene generation, supporting multi-scale scene generation from indoor environments to large-scale urban landscapes.
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Multi-view texture generation: It can generate multi-view consistent texture images, ensuring consistency and realism across different views, and improving visual effects and diversity.
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PBR material generation: It directly learns material decomposition from multi-view images to generate highly realistic PBR materials, ensuring realistic visual effects under different lighting conditions.
Technical Principles of Seed3D 1.0
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Diffusion Transformer architecture: It adopts the Diffusion Transformer model architecture and is trained on large-scale data, enabling efficient generation of high-quality 3D geometry and textures.
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High-precision geometry generation: Through a VAE encoder and Diffusion Transformer model, it achieves high-precision construction of 3D geometry, generating precise structural details and physical integrity.
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Multi-view texture generation: Based on a multimodal Diffusion Transformer architecture, it generates multi-view consistent texture images, ensuring consistency across different views.
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PBR material generation: It adopts an estimation method framework to learn material decomposition from multi-view images and generate highly realistic PBR materials, ensuring realistic visual effects under different lighting conditions.
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Data processing and preprocessing: A complete three-stage data processing pipeline was built to transform massive heterogeneous raw 3D data into high-quality training sets, including preprocessing steps such as data deduplication, pose normalization, and category annotation.
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End-to-end generation: It achieves end-to-end generation from a single image to a simulation-level 3D model, estimating and adjusting the model's scale through a vision-language model (VLM) so that it conforms to real-world physical dimensions.
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Scene generation: Through a step-by-step generation strategy, expand from generating a single object to generating a complete scene, use a vision-language model to extract object and spatial relationship information, construct a scene layout map, and assemble the complete scene.
Project address of Seed3D 1.0
- Project official website：https://seed.bytedance.com/zh/seed3d
- Technical report：https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed3d.pdf
Application scenarios of Seed3D 1.0
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Physics simulation and robot training: The generated 3D models can be directly integrated into physics simulation environments (such as Isaac Sim) for robot operation testing and physics simulation, supporting embodied intelligence large model training.
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Complete 3D scene generation: It can expand from single-object generation to building complete 3D scenes, including indoor environments and large-scale urban landscapes, providing scene content support for world simulators.
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Content creation and design: Provide creative designers with a tool to efficiently generate high-quality 3D models, support generating detailed 3D assets from a single image, and improve creative efficiency.
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Education and training: Generate realistic 3D scenes and models for the education and training field, providing an immersive teaching experience.
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Game development: Quickly generate high-quality 3D game assets and scenes, reduce the workload of manual modeling, and accelerate the game development process.