Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets
Authors: name: Jiashi Feng, name: Xiu Li, name: Jing Lin, name: Jiahang Liu, name: Gaohong Liu, name: Weiqiang Lou, name: Su Ma, name: Guang Shi, name: Qinlong Wang, name: Jun Wang, name: Zhongcong Xu, name: Xuanyu Yi, name: Zihao Yu, name: Jianfeng Zhang, name: Yifan Zhu, name: Rui Chen, name: Jinxin Chi, name: Zixian Du, name: Li Han, name: Lixin Huang, name: Kaihua Jiang, name: Yuhan Li, name: Guan Luo, name: Shuguang Wang, name: Qianyi Wu, name: Fan Yang, name: Junyang Zhang, name: Xuanmeng Zhang
Organization: ByteDance Seed

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