用流模型实现稳定结构保持的图像与3D形状变形。
FLOWING: Implicit Neural Flows for Structure-Preserving Morphing
- 将形变建模为微分向量流，确保连续性与可逆性。
- 在人脸、图像及高斯点云变形中达到最佳质量，收敛更快。
- 适合需要高质量连续形变的应用，如动画与3D重建。
形变是视觉与计算机图形学中的经典问题，需依赖时间相关的坐标变换实现特征对齐，并通过混合实现平滑插值。近年来，多层感知机（MLPs）作为隐式神经表示（INRs）被用于建模此类形变，因其无网格和可微特性；然而，从标准MLP中提取一致且准确的形变通常依赖昂贵的正则化，常导致训练不稳定并影响特征对齐效果。为此，我们提出FLOWING（FLOW morphing），将形变重构为微分向量流的构建，通过直接在网络架构中编码结构流特性，自然保证连续性、可逆性与时间一致性。该流中心方法生成原理清晰且稳定的变换，实现了2D图像与3D形状的精确、结构保持形变。大量实验表明，无论在人脸与图像形变，还是高斯点云形变任务中，FLOWING均取得当前最优形变质量，且收敛速度更快。代码与预训练模型见 http://schardong.github.io/flowing。
原文摘要 · Abstract (English)
Morphing is a long-standing problem in vision and computer graphics, requiring a time-dependent warping for feature alignment and a blending for smooth interpolation. Recently, multilayer perceptrons (MLPs) have been explored as implicit neural representations (INRs) for modeling such deformations, due to their meshlessness and differentiability; however, extracting coherent and accurate morphings from standard MLPs typically relies on costly regularizations, which often lead to unstable training and prevent effective feature alignment. To overcome these limitations, we propose FLOWING (FLOW morphING), a framework that recasts warping as the construction of a differential vector flow, naturally ensuring continuity, invertibility, and temporal coherence by encoding structural flow properties directly into the network architectures. This flow-centric approach yields principled and stable transformations, enabling accurate and structure-preserving morphing of both 2D images and 3D shapes. Extensive experiments across a range of applications - including face and image morphing, as well as Gaussian Splatting morphing - show that FLOWING achieves state-of-the-art morphing quality with faster convergence. Code and pretrained models are available at http://schardong.github.io/flowing.
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