Seed3D is a generative foundation model developed by ByteDance's Seed team, designed to produce high-fidelity, simulation-ready 3D assets from a single 2D image. Unlike traditional 3D generators that focus primarily on visual representation, Seed3D is engineered to create models with watertight, manifold geometry and physically based rendering (PBR) materials. This focus on structural integrity allows the generated assets to be directly integrated into physics engines such as NVIDIA Isaac Sim for robot training and embodied AI research.
The model's architecture is built on a Diffusion Transformer (DiT) framework, utilizing a stepwise generation pipeline to handle complex details. It incorporates a specialized 3D VAE encoder to learn compact geometric representations and a rectified flow-based diffusion process to refine surface features. For texture generation, the system employs a multimodal approach to ensure perspective consistency and high-resolution material estimation, supporting textures up to 4K resolution.
Beyond individual object generation, Seed3D supports stepwise scene composition. By leveraging a vision-language model (VLM) to analyze spatial relationships and object-level cues within an input image, the system can synthesize and assemble multiple assets into a coherent environment. This capability allows users to scale from single-item generation to the construction of entire functional scenes, ranging from indoor office spaces to complex urban street environments.