Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets
Jiashi Feng, Xiu Li, Jing Lin, Jiahang Liu, Gaohong Liu, Weiqiang Lou, Su Ma, Guang Shi, Qinlong Wang, Jun Wang, Zhongcong Xu, Xuanyu Yi, Zihao Yu, Jianfeng Zhang, Yifan Zhu, Rui Chen, Jinxin Chi, Zixian Du, Li Han, Lixin Huang, Kaihua Jiang, Yuhan Li
2025-10-24
Summary
This paper introduces Seed3D 1.0, a new AI model that creates 3D objects for use in realistic simulations, like those used to train robots or test physics.
What's the problem?
Creating realistic and diverse environments for training AI agents is really hard. Existing methods either create visually appealing worlds that don't follow the rules of physics, or they create physically accurate worlds but are limited because building all the 3D objects by hand takes a lot of time and effort. It's a trade-off between looking good and being accurate, and scaling up either approach is difficult.
What's the solution?
The researchers developed Seed3D 1.0, which can generate 3D models from just a single image. These aren't just any 3D models; they're designed to work seamlessly with physics engines, meaning they behave realistically when interacted with. The system can create individual objects and even entire scenes by combining these objects, making it much faster to build complex simulation environments.
Why it matters?
Seed3D 1.0 is important because it removes a major bottleneck in developing AI agents that interact with the physical world. By making it easier and faster to create realistic simulation environments, it allows researchers to train more capable robots and AI systems, and ultimately advance the field of physics-based world simulators.
Abstract
Developing embodied AI agents requires scalable training environments that balance content diversity with physics accuracy. World simulators provide such environments but face distinct limitations: video-based methods generate diverse content but lack real-time physics feedback for interactive learning, while physics-based engines provide accurate dynamics but face scalability limitations from costly manual asset creation. We present Seed3D 1.0, a foundation model that generates simulation-ready 3D assets from single images, addressing the scalability challenge while maintaining physics rigor. Unlike existing 3D generation models, our system produces assets with accurate geometry, well-aligned textures, and realistic physically-based materials. These assets can be directly integrated into physics engines with minimal configuration, enabling deployment in robotic manipulation and simulation training. Beyond individual objects, the system scales to complete scene generation through assembling objects into coherent environments. By enabling scalable simulation-ready content creation, Seed3D 1.0 provides a foundation for advancing physics-based world simulators. Seed3D 1.0 is now available on https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?modelId=doubao-seed3d-1-0-250928&tab=Gen3D