统一多种人体模型，让不同格式数据自由混用。
SOMA: Unifying Parametric Human Body Models
- 通过三重抽象层实现不同人体模型间的无缝转换。
- 单次计算即可完成任意姿态下的骨骼变换，无需迭代优化。
- 支持端到端可微分与GPU加速，适合研究与工业级应用。
参数化人体模型是人体重建、动画与模拟的基础，但SMPL、SMPL-X、MHR、Anny等模型在网格拓扑、骨骼结构、形状参数化和单位规范上互不兼容，难以在单一流程中融合其优势。我们提出SOMA，一个统一的人体层，通过三层抽象实现异构表示的桥接：网格拓扑抽象以常数时间将任意源模型身份映射到统一标准网格；骨骼抽象在单次闭式运算中恢复任意体型与姿态下的完整关节变换，无需迭代优化或模型专属训练；姿态抽象逆向皮肤化流程，直接从任意支持模型的变形顶点中恢复统一的骨骼旋转，使异构运动数据集可直接使用而无需定制重定向。三者结合将每对模型间的适配复杂度从O(M²)降至O(M)单后端连接，使用户可在推理时自由混合身份来源与动作数据。整个流程完全可微分且基于NVIDIA-Warp实现GPU加速。
原文摘要 · Abstract (English)
Parametric human body models are foundational to human reconstruction, animation, and simulation, yet they remain mutually incompatible: SMPL, SMPL-X, MHR, Anny, and related models each diverge in mesh topology, skeletal structure, shape parameterization, and unit convention, making it impractical to exploit their complementary strengths within a single pipeline. We present SOMA, a unified body layer that bridges these heterogeneous representations through three abstraction layers. Mesh topology abstraction maps any source model's identity to a shared canonical mesh in constant time per vertex. Skeletal abstraction recovers a full set of identity-adapted joint transforms from any body shape, whether in rest pose or an arbitrary posed configuration, in a single closed-form pass, with no iterative optimization or per-model training. Pose abstraction inverts the skinning pipeline to recover unified skeleton rotations directly from posed vertices of any supported model, enabling heterogeneous motion datasets to be consumed without custom retargeting. Together, these layers reduce the $O(M^2)$ per-pair adapter problem to $O(M)$ single-backend connectors, letting practitioners freely mix identity sources and pose data at inference time. The entire pipeline is fully differentiable end-to-end and GPU-accelerated via NVIDIA-Warp.
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