从一张图生成可直接用于物理模拟的高保真3D资产
Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets
- 输入单张图像，自动生成带真实材质与几何结构的3D资产
- 生成资产可零配置接入物理引擎，支持机器人操作训练
- 支持单体物体到完整场景的规模化生成，适合仿真训练
构建具身智能体需要可扩展的训练环境，在内容多样性与物理准确性之间取得平衡。世界模拟器提供此类环境，但面临不同局限：基于视频的方法生成内容多样，但缺乏实时物理反馈；基于物理引擎的方法虽能精准模拟动态行为，却受限于高成本的人工资产创建。本文提出Seed3D 1.0，一个从单张图像生成可直接用于模拟的3D资产的基础模型，解决了可扩展性问题并保持物理真实性。与现有3D生成模型不同，该系统生成的资产具备精确几何、对齐纹理和真实物理材质。这些资产可极低配置地集成至物理引擎中，适用于机器人抓取与仿真训练。系统还可将多个物体组装为连贯场景，实现完整环境的规模化生成。通过实现可扩展的仿真就绪内容生成，Seed3D 1.0为推进基于物理的世界模拟器奠定了基础。当前已在火山引擎平台上线，地址：https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?modelId=doubao-seed3d-1-0-250928&tab=Gen3D
原文摘要 · Abstract (English)
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
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