ByteDance launches Seed3D 1.0, a tool for creating large-scale 3D models from a single image, achieving simulation-grade quality.

On October 23rd, ByteDance's Seed team launched the 3D generation large model - Seed3D 1.0, which achieves end-to-end generation of high-quality simulation-level 3D models from a single image. Seed3D 1.0 is based on the innovative Diffusion Transformer architecture, trained with large-scale data, and can generate complete 3D models including fine geometry, real textures, and physically based rendering (PBR) materials. It is reported that the 3D models generated by Seed3D 1.0 can be seamlessly imported into simulation engines such as Isaac Sim with minimal adaptation work to support large-scale intelligent model training. In addition, through step-by-step scene generation, Seed3D 1.0 can generate from a single object to building complete 3D scenes. In comparison with existing 3D generation models, Seed3D 1.0 demonstrates advantages: its texture and material generation performance exceeds previous open source and closed source models, geometric generation performance surpasses models with larger parameter scales in the industry, and its overall capability reaches a leading level in the industry. To verify the generation quality of Seed3D 1.0, the team conducted comprehensive comparative evaluations covering the two core tasks of geometric generation and material texture generation, evaluating the model's performance through quantitative benchmark tests, qualitative analysis, and human evaluations from multiple dimensions. The evaluation results show that Seed3D 1.0 excels in detail preservation and structural integrity, and the 1.5B parameter Seed3D 1.0 surpasses the industry's 3B parameter model (Hunyuan3D-2.1) in performance, accurately reproducing the fine features of complex objects. Furthermore, using a step-by-step generation strategy, Seed3D 1.0 can expand from generating a single object to generating complete, coherent 3D scenes. The Seed team stated that they will try to introduce multimodal large language models (MLLM) in the future to enhance the quality and robustness of 3D generation, and promote the large-scale application of 3D generation models in world simulators.