ByteDance Unveils “Seed3D 1.0”: Single Image to High-Fidelity 3D Model Generation
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ByteDance launches Seed3D 1.0, a large 3D-generation model that turns a single image into high-fidelity 3D objects with realistic textures and full scenes.
ByteDance’s Seed team today launched the large-scale 3D generation model Seed3D 1.0, capable of producing high-quality, physically-rendered 3D models from a single image.
The model uses an innovative Diffusion Transformer architecture and is trained on massive datasets to generate full 3D geometry, realistic textures, and PBR (physically based rendering) materials. According to the official introduction, objects created with Seed3D 1.0 can be seamlessly imported into simulation engines such as Isaac Sim, requiring little adaptation before being used in embodied intelligence model training.
In terms of performance, the Seed team claims that the model’s texture-and-material generation capabilities outperform both open- and closed-source alternatives, and that its geometry generation exceeds that of industry models with larger parameter counts—specifically, the 1.5 billion-parameter Seed3D 1.0 outperformed a 3 billion-parameter competitor (Hunyuan3D-2.1).
Furthermore, Seed3D 1.0 supports stepwise scene generation: after inputting one object image, the system can expand to a full 3D scene—first extracting objects and spatial relations via a vision-language model, then generating each object’s 3D model, and finally assembling them into a complete environment (from office spaces to city street scenes).
With the launch of Seed3D 1.0, ByteDance advances beyond standard image and text generation models into sophisticated 3D content creation, enabling use-cases in simulation, robotics, gaming and industrial virtualisation. By supporting full scenes and high-fidelity texture/geometry generation, the model positions ByteDance in competition with other global generative-AI efforts for 3D modeling.
