ByteDance Seed team recently announced the launch of the 3D generation large model Seed3D 1.0, which can end-to-end generate high-quality simulation-level 3D models from a single image, including detailed geometry, realistic textures, and physically based rendering (PBR) materials. This innovative achievement is expected to provide powerful world simulator support for the development of embodied intelligence, addressing bottlenecks in physical interaction capabilities and content diversity in current technology.

In the development process, the Seed team collected and processed large-scale high-quality 3D data, building a complete three-stage data processing pipeline that transformed massive heterogeneous raw 3D data into a high-quality training set. Seed3D 1.0 uses a model based on the Diffusion Transformer architecture, achieving fast generation from a single image to a simulation-level 3D model through an end-to-end technical approach. The model performs well in geometric generation, accurately constructing structural details and ensuring physical integrity; in texture map generation, it ensures consistency between different perspectives through a multimodal Diffusion Transformer architecture; and in PBR material generation, it adopts an estimation method framework, improving the accuracy of material estimation.

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The generative capabilities of Seed3D 1.0 have shown significant advantages in multiple comparative evaluations. In terms of geometric generation, the 1.5B parameter Seed3D 1.0 surpasses industry models with 3B parameters, enabling more accurate restoration of fine features of complex objects. In texture material generation, Seed3D 1.0 performs well in maintaining reference image consistency, especially showing obvious advantages in fine text generation and character generation. Human evaluation results show that Seed3D 1.0 has received good scores in multiple dimensions such as geometric quality, material texture, visual clarity, and detail richness.

Seed3D 1.0 not only generates 3D models of individual objects but also constructs complete 3D scenes through a step-by-step generation strategy. The generated 3D models can be seamlessly imported into simulation engines like Isaac Sim, requiring only minimal adaptation work to support embodied intelligence large model training. This capability provides diverse operational scenarios for robot training, realizes interactive learning, and builds a comprehensive evaluation benchmark for vision-language-action models.

Although Seed3D 1.0 has demonstrated good performance in 3D model and scene generation, the Seed team also recognizes that building a world model based on a 3D generation large model still faces challenges such as improving generation accuracy and generalization. In the future, the team will attempt to introduce multimodal large language models (MLLMs) to enhance the quality and robustness of 3D generation and promote the large-scale application of 3D generation models in world simulators.

Project homepage:

https://seed.bytedance.com/seed3d

Experience entry:

https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?modelId=doubao-seed3d-1-0-250928&tab=Gen3D