mlsanityData to 14 Oct 2026

BaRe-ESA: A Riemannian Framework for Unregistered Human Body Shapes

arXiv 2211.13185 ↗First posted 23 Nov 2022cs.CVcited by 7

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Abstract (arXiv)

We present Basis Restricted Elastic Shape Analysis (BaRe-ESA), a novel Riemannian framework for human body scan representation, interpolation and extrapolation. BaRe-ESA operates directly on unregistered meshes, i.e., without the need to establish prior point to point correspondences or to assume a consistent mesh structure. Our method relies on a latent space representation, which is equipped with a Riemannian (non-Euclidean) metric associated to an invariant higher-order metric on the space of surfaces. Experimental results on the FAUST and DFAUST datasets show that BaRe-ESA brings significant improvements with respect to previous solutions in terms of shape registration, interpolation and extrapolation. The efficiency and strength of our model is further demonstrated in applications such as motion transfer and random generation of body shape and pose.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 23 Nov 2022.

Builds on

26 of 55 references.

References · 26

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
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  5. 2016Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Imagecited by 695
  6. 2018Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimationcited by 185
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  8. 2019Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generationcited by 90
  9. 2022Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fieldscited by 85
  10. 2021NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Gocited by 81
  11. 2020LIMP: Learning Latent Shape Representations with Metric Preservation Priorscited by 55
  12. 2021Shape registration in the time of transformerscited by 51
14 more
  1. 2015Building Statistical Shape Spaces for 3D Human Modelingcited by 47
  2. 2021ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generatorscited by 40
  3. 2019Geometric Disentanglement for Generative Latent Shape Modelscited by 30
  4. 2018FARM: Functional Automatic Registration Method for 3D Human Bodiescited by 26
  5. 2013The varifold representation of non-oriented shapes for diffeomorphic registrationcited by 24
  6. 2021Augmenting Implicit Neural Shape Representations with Explicit Deformation Fieldscited by 19
  7. 2022Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical frameworkcited by 19
  8. 2021GLASS: Geometric Latent Augmentation for Shape Spacescited by 16
  9. 2020A numerical framework for elastic surface matching, comparison, and interpolationcited by 8
  10. 20214D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Datacited by 8
  11. 2021A Riemannian Framework for Analysis of Human Body Surfacecited by 8
  12. 20223D Shape Sequence of Human Comparison and Classification using Current and Varifoldscited by 8
  13. 2021Neural Human Deformation Transfercited by 6
  14. 2022Shape spaces: From geometry to biological plausibilitycited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2023Basis restricted elastic shape analysis on the space of unregistered surfacescited by 5
  2. 2024Self Supervised Networks for Learning Latent Space Representations of Human Body Scans and Motionscited by 3
  3. 2025FUSE: A Flow-based Mapping Between Shapescited by 3
  4. 2024PaNDaS: Learnable Deformation Modeling with Localized Controlcited by 1
  5. 2025ARC-Flow : Articulated, Resolution-Agnostic, Correspondence-Free Matching and Interpolation of 3D Shapes Under Flow Fieldscited by 1
  6. 2025ScanMove: Motion Prediction and Transfer for Unregistered Body Meshescited by 1
  7. 2026ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learningcited by 0