Seed 3D:3D Base Model With Both Physical Simulation Accuracy and Scalability

On October 23, Seed3D 1.0 was released, enabling end-to-end generation from a single image to high-quality simulation-level 3D models.

Seed3D 1.0 uses the Diffusion Transformer, a widely used model architecture in generative AI, to design 3D geometry generation and texture mapping models. In the future, the team will explore introducing a multimodal large language model (MLLM) to improve the quality and robustness of 3D generation and promote the large-scale application of 3D generative models in world simulators.

Core Features of Seed3D AI 3D Technology

Dora-VAE: Advanced 3D Model Reconstruction

Dora-VAE technology achieves 8× compression improvement over traditional methods while maintaining exceptional quality in 3D model generation. This AI 3D approach uses sharp edge sampling to preserve geometric details that matter most in your 3D models.

MagicArticulate: Automatic 3D Model Animation

Transform static 3D models into animation-ready assets automatically. MagicArticulate uses AI to generate skeletal structures and skinning weights, making your 3D models ready for real-time animation without manual rigging.

Inference-Time Scalability

Unique to Seed 3D, our AI 3D model generator allows you to adjust quality from 1,000 to 100,000+ tokens during inference. Scale your 3D model generation quality based on your specific needs without retraining.

Comprehensive 3D Model Dataset

Trained on Articulation-XL 2.0 with 48,000+ high-quality 3D models. Our extensive dataset ensures robust AI 3D generation across diverse object categories and geometric complexities.

Seed3D 1.0 Generation Capabilities

Seed3D 1.0 generation capabilities have demonstrated significant advantages in multiple comparative evaluations. In terms of geometry generation, the 1.5 billion-parameter Seed3D 1.0 surpasses industry-leading models with 3 billion parameters, enabling more accurate reproduction of the fine features of complex objects. In terms of texture generation, Seed3D 1.0 excels in maintaining consistency with reference images, with a particularly strong advantage in fine text and character generation. Human evaluation results show that Seed3D 1.0 receives excellent scores across multiple dimensions, including geometric quality, material texture, visual clarity, and richness of detail.

Seed3D 1.0 Diversified Expansion

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 to support the training of large-scale embodied intelligence models. This functionality provides a rich set of operational scenarios for robot training, enables interactive learning, and establishes a comprehensive evaluation benchmark for vision-language-action models.

Future Outlook of seed3d

Building a world model based on a large 3D generative model still faces challenges such as improving generation accuracy and generalization capabilities. In the future, the team will try to introduce a multimodal large language model (MLLM) to improve the quality and robustness of 3D generation and promote the large-scale application of 3D generative models in world simulators.