SmoCap: Unified Scale-Pose Canonicalization with Proxy-Mapped Trust-Region QP

• URL: http://arxiv.org/abs/2605.20850v1
• Date: Wed, 20 May 2026 07:41:18 GMT
• Title: SmoCap: Unified Scale-Pose Canonicalization with Proxy-Mapped Trust-Region QP
• Abstract summary: We present SmoCap, a leakage-resistant canonicalization framework.<n>It estimates morphology and posture jointly in each local trust-region quadratic program.
• Score: 3.4350080125227564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: Stage-wise workflows that separate model scaling and inverse kinematics can induce morphology-posture compensation, resulting in anatomically inconsistent yet numerically acceptable solutions, especially in weakly observed directions. We present SmoCap, a leakage-resistant canonicalization framework that estimates morphology and posture jointly in each local trust-region quadratic program (QP) within a sparse control subspace. Methods: SmoCap solves a constrained trust-region QP with analytical proxy-mapped pose and scale Jacobians. The low dimensional proxy map stabilizes weakly observed directions and drives coordinated structures. An optional pre-solve provides warm starts in difficult configurations. The framework is evaluated using cohort fluoroscopy knee motion, anthropometric ground truth, and extreme yoga sequences. Results: SmoCap achieved 2.9 degree knee flexion RMSE against fluoroscopy, and a pooled anthropometric endpoint error around 3%. In the leakage audit against segment wise scaling, SmoCap also reduced marker RMSE, FE error, and anthropometric endpoint error. Proxy coupling preserved expressive and coordinated spine motion with marginal fitting error increase (+0.14 mm, +0.6%) against baseline models in yoga ablation. Median marker RMSE was around 20 mm, and median runtime was 0.204-0.332 ms/frame, achieved with consistently 2-3 iterations. Conclusion: SmoCap provides an externally validated unified coupling-aware scale-pose framework, making externally consistent motion canonicalization practical at dataset scale.

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