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.

翻译：参数化人体模型是人体重建、动画与仿真的基础，然而现有模型之间仍存在互不兼容的问题：SMPL、SMPL-X、MHR、Anny及相关模型在网格拓扑、骨骼结构、形状参数化与单位约定等方面均存在差异，导致难以在单一流程中综合利用其互补优势。本文提出SOMA——一个通过三层抽象机制桥接异构表征的统一人体模型层。网格拓扑抽象层可在恒定时间内将任意源模型的身份特征映射至共享规范网格；骨骼抽象层通过单次封闭式计算（无需迭代优化或针对特定模型的训练），从任意静止姿态或任意姿态配置的人体形状中恢复完整的身份自适应关节变换；姿态抽象层通过逆向蒙皮流程，直接从任意支持模型的姿态顶点中恢复统一骨骼旋转，使得异构运动数据集无需定制重定向即可被使用。这些抽象层共同将$O(M^2)$的模型配对适配问题简化为$O(M)$的单后端连接器，使实践者能够在推理阶段自由混合身份源与姿态数据。整个流程通过NVIDIA Warp实现端到端完全可微且GPU加速。