Seed3D 1.0 – ByteDance’s large-scale 3D generation model

What is Seed3D 1.0？

Seed3D 1.0 is a large-scale 3D generation model released by ByteDance’s Seed team. It can generate high-precision 3D models from a single image and features high-fidelity asset generation, physics engine compatibility, and scalable scene composition. Based on the innovative Diffusion Transformer architecture and trained on large-scale datasets, it can produce detailed geometric structures, realistic textures, and physically-based rendering (PBR) materials. In performance evaluations, Seed3D 1.0 demonstrates excellent results in both geometry and texture generation, particularly excelling in preserving fine details and reconstructing complex features. The generated 3D models can be directly integrated into physics simulation environments for tasks such as robotic manipulation testing. Its capabilities extend from generating individual objects to complete scenes, providing strong support for embodied intelligence world simulators.

Key Features of Seed3D 1.0

• High-Fidelity Asset Generation: Generates detailed geometric structures, realistic textures, and PBR materials from a single image, producing high-precision and high-quality 3D models.

• Physics Engine Compatibility: The generated 3D models can be directly integrated into industry-standard physics simulation environments (e.g., Isaac Sim) for physics simulation and robotic manipulation testing, supporting real-time simulation.

• Scalable Scene Composition: Using a decomposed generation approach, it can scale from single-object generation to complete scene generation, supporting multi-scale environments from indoor spaces to large urban landscapes.

• Multi-View Consistent Texture Generation: Produces textures consistent across multiple views, ensuring realism and visual consistency, enhancing both appearance and diversity.

• PBR Material Generation: Learns material decomposition directly from multi-view images to generate highly realistic PBR materials, maintaining visual fidelity under varying lighting conditions.

Technical Principles of Seed3D 1.0

• Diffusion Transformer Architecture: Built on a Diffusion Transformer model trained on large-scale datasets, efficiently generating high-quality 3D geometry and textures.

• High-Precision Geometry Generation: Combines a VAE encoder with the Diffusion Transformer to construct precise 3D geometry, preserving detailed structures and physical integrity.

• Multi-View Texture Generation: Uses a multi-modal Diffusion Transformer to produce textures consistent across multiple views.

• PBR Material Generation: Employs an estimation framework to learn material decomposition from multi-view images, producing highly realistic PBR materials for accurate rendering under different lighting.

• Data Processing and Preprocessing: Implements a three-stage data pipeline to convert massive heterogeneous raw 3D data into high-quality training sets, including deduplication, pose normalization, and category annotation.

• End-to-End Generation: Achieves end-to-end generation from a single image to a simulation-grade 3D model, with a Vision-Language Model (VLM) estimating and adjusting model scale to match real-world physical dimensions.

• Scene Generation: Uses a stepwise generation strategy to scale from individual objects to full scenes, leveraging VLMs to extract object and spatial relationship information, construct scene layouts, and assemble complete scenes.

Project Links for Seed3D 1.0

• Official Website: https://seed.bytedance.com/zh/seed3d

• Technical Report: https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/seed3d.pdf

Applications of Seed3D 1.0

• Physics Simulation and Robot Training: Generated 3D models can be directly integrated into physics simulation environments (e.g., Isaac Sim) for robotic manipulation testing and physics simulations, supporting embodied intelligence model training.

• Complete 3D Scene Generation: Extends from single-object generation to complete 3D scenes, including indoor environments and large-scale urban landscapes, providing scene content for world simulators.

• Content Creation and Design: Offers designers an efficient tool to generate high-quality 3D assets from single images, improving creative workflows.

• Education and Training: Creates realistic 3D scenes and models for immersive educational and training experiences.

• Game Development: Rapidly produces high-quality 3D game assets and environments, reducing manual modeling workload and accelerating game development pipelines.