用神经场与变形模型结合，重建松垮衣物等非刚体运动的高保真3D动态。
NF3DM: Combining Neural Fields and Deformation Models for 3D Non-Rigid Motion Reconstruction
- 将神经场与显式网格变形模型融合，统一建模形状与运动。
- 在单目深度视频上实现优于现有方法的重建质量。
- 适合人体、动物等近等距形变场景的高精度动作捕捉。
我们提出一种新颖的数据驱动方法，从无结构且可能不完整的非刚体形变物体观测中重建时序一致的3D运动。目标是实现近等距形变（如穿宽松衣物的人体）的高保真运动重建。核心创新在于将隐式形状表示与显式网格变形模型相结合，无需参数化形状模型或分离形状与运动。每帧由特征空间中的时序观测融合生成的神经场表示，从而保留输入数据中的几何细节。通过施加相邻帧间近等距形变约束，确保神经场底层表面的时序一致性。实验表明，该方法在单目深度视频上对人和动物运动序列的重建性能优于现有先进方法。
原文摘要 · Abstract (English)
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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