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New framework refines 3D human body shape models

Researchers have developed a new framework to refine 3D human body shape reconstructions from parametric models like SMPL. The framework combines three methods: Latent-VaRefine for correcting latent codes, Vertex-VaRefine for predicting vertex displacements using DiffusionNet, and ElaOpt for direct surface deformation optimization. This approach significantly reduces reconstruction errors, demonstrating adaptability across different body models such as SMPL-H and SMPL-X, and robustness to variations in mesh discretization and geometry. AI

IMPACT Improves geometric fidelity in 3D human body modeling, potentially aiding applications in animation, virtual reality, and biomechanics.

RANK_REASON The cluster contains an academic paper detailing a new framework for refining 3D human body shape models.

Read on Hugging Face Daily Papers →

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New framework refines 3D human body shape models

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The cluster contains an academic paper detailing a new framework for refining 3D human body shape models.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Mesh-Agnostic Refinement Models for Latent Space Encoding of 3D Human Body Shapes

    Parametric human body models such as the Skinned Multi-Person Linear (SMPL) model provide compact representations of human shape and pose, but their reconstructions remain affected by both errors in estimated latent parameters and limitations in the decoder's expressive capacity.…

  2. arXiv cs.CV TIER_1 English(EN) · Chaima Zouaoui, Jonathan Cerqueira, Sylvain Arguillere, Martin Bauer, Nicolas Charon, Mohamed Daoudi, Emmanuel Hartman ·

    Mesh-Agnostic Refinement Models for Latent Space Encoding of 3D Human Body Shapes

    arXiv:2611.03285v2 Announce Type: replace Abstract: Parametric human body models such as the Skinned Multi-Person Linear (SMPL) model provide compact representations of human shape and pose, but their reconstructions remain affected by both errors in estimated latent parameters a…