SOMA: Unifying Parametric Human Body Models

Abstract

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.

Figures (11)

• Figure 1: SOMA unifies five heterogeneous parametric body models (SOMA-Shape, MHR, SMPL-X, Anny, and GarmentMeasurements) under a single animation pipeline. (a) Unified Skeleton: despite originating from entirely different identity spaces, joint hierarchies, and mesh resolutions, all five models are driven by the same SOMA skeleton in an identical pose, with no model-specific retargeting. (b) Unified Pose Correctives: a single MLP correctives model trained once on the shared SOMA topology produces anatomically plausible pose-dependent deformations for all backends, mitigating standard LBS artifacts without per-model corrective learning. (c) Unified Mesh Topology: all identity models share the same mesh structure, enabling skinning weights, deformation priors, and correctives to transfer seamlessly across backends.
• Figure 2: Overview of SOMA. SOMA decouples body identity from pose through three sequential layers. Identity Model Provider (left): any supported backend (SOMA-shape, Anny, MHR, SMPL/SMPL-X, or GarmentMeasurements) maps its own shape parameters $\beta_s$ to a rest-shape mesh in its native topology. Bridging Layer (middle): two abstraction steps canonicalize the source identity into a unified representation. Mesh Topology Abstraction transfers the rest shape to the shared SOMA topology via pre-computed barycentric coordinates; Skeletal Abstraction then fits the shared $J{=}77$-joint SOMA rig to the transferred rest shape in a single closed-form pass, with no iterative optimization or per-identity training. Animation Layer (right): all identity models are animated through the shared SOMA skeleton using $\theta_{\text{SOMA}}$ joint rotations. When motion data arrives in another model's convention, i.e.$\theta_\mathrm{x} \in \{\theta_{\text{MHR}}, \theta_{\text{SMPL}}, \ldots\}$, Pose Abstraction converts it to $\theta_{\text{SOMA}}$ by analytically inverting the LBS pipeline; this step is bypassed when pose is already in the SOMA convention. A shared MLP Pose Correctives model then predicts pose-dependent vertex displacements to correct LBS artifacts, and Linear Blend Skinning produces the final posed mesh. The entire pipeline is fully differentiable end-to-end.
• Figure 3: Training data for the SOMA-Shape PCA model. (a) A subset of the 9,326 SizeUSA body scans registered to the SOMA topology, exhibiting a wide range of body weights and proportions. (b) A subset of the 303 Triplegangers scans, registered to the same topology. All meshes are reposed to a canonical A-pose before PCA fitting, and mirror-augmented across the sagittal plane to enforce bilateral symmetry.
• Figure 4: Mesh topology abstraction. Top: native mesh topologies of each identity model. Bottom: the same identities mapped to the shared SOMA topology via 3D barycentric interpolation. This common mesh serves as the pivot for all cross-model operations---skeleton fitting, pose transfer, correctives, and shape-space comparison all operate on a single canonical topology regardless of the source model.
• Figure 5: Skeleton fitting on posed SAM 3D Body identities. Eight MHR identities in diverse poses with the SOMA skeleton fitted via SkeletonTransfer (\ref{['sec:skeleton']}). Unlike joint regressors that assume a rest pose, our method generalizes to arbitrary posed shapes: joint positions are regressed via RBF interpolation, and joint rotations are recovered by Procrustes alignment, both in a single analytical forward pass with no iterative optimization.