We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulation-ready capabilities, and application coverage. For geometry, a coarse-to-fine two-stage pipeline decouples global structure learning from high-frequency detail recovery, while a locality-aware VAE achieves higher spatial compression and more efficient decoding. For texture and material generation, we replace the cascaded pipeline of Seed3D 1.0 with a unified PBR model that directly generates multi-view albedo and metallic-roughness maps, enhanced by Mixture-of-Experts scaling and VLM-based semantic conditioning for improved material precision and visual fidelity. Beyond single-object generation, Seed3D 2.0 introduces a simulation-ready model suite comprising scene layout planning, part-aware decomposition, and training-free articulation generation, enabling coherent scene construction and part-level physical interaction across physics and graphics engines. A large-scale human preference study against five recent commercial models shows that Seed3D 2.0 achieves consistent win rates of 69.0% to 89.9% in textured 3D asset generation. Seed3D 2.0 is available on https://exp.volcengine.com/ark/vision?_vtm_=0.0.c70961.d701978.0&mode=vision&modelId=doubao-seed3d-2-0-260328&tab=Gen3D

翻译：我们提出 Seed3D 2.0，这是一个基于 Seed3D 1.0 构建的先进三维内容生成系统，在生成保真度、仿真就绪能力及应用覆盖范围方面均实现了显著提升。在几何结构方面，采用由粗到精的两阶段流水线将全局结构学习与高频细节恢复分离，同时引入局部感知变分自编码器，实现更高空间压缩比与更高效解码。在纹理与材质生成方面，我们用统一的物理渲染模型替代 Seed3D 1.0 的级联流水线，直接生成多视角反照率图与金属-粗糙度图，并通过混合专家扩展和基于视觉语言模型的语义约束增强材质精度与视觉保真度。除单物体生成外，Seed3D 2.0 推出了一套仿真就绪模型套件，包含场景布局规划、部件感知分解及免训练关节生成功能，支持跨物理与图形引擎的连贯场景构建及部件级物理交互。一项针对五种近期商用模型的大规模人类偏好研究表明，Seed3D 2.0 在带纹理三维资产生成任务中实现了 69.0% 至 89.9% 的稳定胜率。Seed3D 2.0 可通过 https://exp.volcengine.com/ark/vision?_vtm_=0.0.c70961.d701978.0&mode=vision&modelId=doubao-seed3d-2-0-260328&tab=Gen3D 获取。