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

On Mon, Oct 23, 2023, Seed3D 1.0 was launched, enabling comprehensive generation from a single image to high-quality simulation-level 3D models.

Seed3D 1.0 employs the Diffusion Transformer, a model architecture prevalent in generative AI, for the development of 3D geometry generation and texture mapping models. Future plans include exploring the integration of a multimodal large language model (MLLM) to enhance the quality and robustness of 3D generation and facilitate the widespread application of 3D generative models in world simulators.

Core Features of Seed3D AI 3D Technology

Dora-VAE: Advanced 3D Model Reconstruction

Dora-VAE technology offers an 8× compression improvement over traditional methods while maintaining high quality in 3D model generation. This AI 3D approach employs sharp edge sampling to preserve essential geometric details.

MagicArticulate: Automatic 3D Model Animation

MagicArticulate automatically transforms static 3D models into animation-ready assets. It utilizes AI to generate skeletal structures and skinning weights, facilitating real-time animation without manual rigging.

Unique to Seed 3D, the AI 3D model generator allows quality adjustment from 1,000 to 100,000+ tokens during inference, enabling scalable 3D model generation based on specific needs without retraining.

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

Seed3D 1.0 demonstrates significant advantages in comparative evaluations. In geometry generation , the 1.5 billion-parameter Seed3D 1.0 surpasses models with 3 billion parameters, enabling more accurate reproduction of complex objects. In texture generation , it excels in maintaining consistency with reference images, especially in fine text and character generation. Human evaluation results indicate excellent scores in geometric quality, material texture, visual clarity, and detail richness.

Seed3D 1.0 generates both individual 3D models and complete 3D scenes using a step-by-step strategy. Generated models can be seamlessly integrated into simulation engines like Isaac Sim, requiring minimal adaptation to support the training of large-scale embodied intelligence models. This capability provides extensive operational scenarios for robot training, enables interactive learning, and establishes a comprehensive evaluation benchmark for vision-language-action models.

Challenges remain in building a world model based on a large 3D generative model, particularly in improving generation accuracy and generalization capabilities. Future efforts will focus on introducing a multimodal large language model (MLLM) to enhance 3D generation quality and robustness, promoting the large-scale application of 3D generative models in world simulators.

Based on reporting by TechBullion.