Combining Neural Fields and Deformation Models for Non-Rigid 3D Motion Reconstruction from Partial Data

• URL: http://arxiv.org/abs/2412.08511v1
• Date: Wed, 11 Dec 2024 16:24:08 GMT
• Title: Combining Neural Fields and Deformation Models for Non-Rigid 3D Motion Reconstruction from Partial Data
• Authors: Aymen Merrouche, Stefanie Wuhrer, Edmond Boyer,
• Abstract summary: We introduce a novel, data-driven approach for reconstructing temporally coherent 3D motion from unstructured observations of non-rigidly deforming shapes. Our goal is to achieve high-fidelity motion reconstructions for shapes that undergo near-isometric deformations, such as humans wearing loose clothing. Our method outperforms state-of-the-art approaches, as demonstrated by its application to human and animal motion sequences reconstructed from monocular depth videos.
• Score: 7.327850781641328
• License:
• Abstract: We introduce a novel, data-driven approach for reconstructing temporally coherent 3D motion from unstructured and potentially partial observations of non-rigidly deforming shapes. Our goal is to achieve high-fidelity motion reconstructions for shapes that undergo near-isometric deformations, such as humans wearing loose clothing. The key novelty of our work lies in its ability to combine implicit shape representations with explicit mesh-based deformation models, enabling detailed and temporally coherent motion reconstructions without relying on parametric shape models or decoupling shape and motion. Each frame is represented as a neural field decoded from a feature space where observations over time are fused, hence preserving geometric details present in the input data. Temporal coherence is enforced with a near-isometric deformation constraint between adjacent frames that applies to the underlying surface in the neural field. Our method outperforms state-of-the-art approaches, as demonstrated by its application to human and animal motion sequences reconstructed from monocular depth videos.

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