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Integrating 3D Posture Neutralization with Statistical Shape Modeling for Head-Neck-Shoulder Ergonomic Design

arXiv 2610.17671 ↗First posted 13 Oct 2026cs.GR cs.CVcited by 0

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Abstract (arXiv)

The design of ergonomic products and workspaces requires a deep understanding of human anatomy and posture. In particular, the head-neck-shoulder region is critical for many applications such as cervical orthoses, helmets, and other head-worn or neck-worn devices. This paper presents a framework that integrates posture neutralization with statistical shape modeling (SSM) for the head-neck-shoulder (HNS) region. We acquire a single three-dimensional (3D) scan of the head-neck-shoulder region from each of 200 Chinese adults in a natural posture for each subject. Every scan is then posture-neutralized before SSM. We fit a parametric head-neck model to each scan to separate cervical pose from anatomy, and reset the cervical pose to a common neutral configuration, so that the resulting model captures anatomical shape variation rather than posture-induced deformation. The resulting shape space yields a standardized template and population-wide modes of variation, providing baselines for parametric ergonomic design. We further discuss how the model supports population coverage analysis and the design of products that accommodate anatomical diversity while maintaining neutral cervical posture.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 62 references that are in the map. First posted on arXiv 13 Oct 2026.

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References · 6

  1. 2017How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)cited by 525
  2. 20193D Morphable Face Models -- Past, Present and Futurecited by 270
  3. 2021FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstructioncited by 36
  4. 2022REALY: Rethinking the Evaluation of 3D Face Reconstructioncited by 33
  5. 2023HACK: Learning a Parametric Head and Neck Model for High-fidelity Animationcited by 8
  6. 2026GNM Head: A Generative aNthropometric Model of the human headcited by 2

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  3. 2025Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface Meshes
  4. 2023Fully Bayesian VIB-DeepSSM
  5. 2026SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp
  6. 2022From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach
  7. 2024MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images
  8. 2026High-Fidelity Medical Shape Generation via Skeletal Latent Diffusion