Handle-based mesh deformation is a classic paradigm in computer graphics which enables intuitive edits from sparse controls. Classical techniques are fast and precise, but require users to know ideal handle placement apriori, which can be unintuitive and inconsistent. Handle sets cannot be adjusted easily, as weights are typically optimized through energies defined by the handles. Modern data-driven methods, on the other hand, provide semantic edits but sacrifice fine-grained control and speed. We propose a technique that achieves the best of both worlds: deep feature proximity yields smooth, visual-aware deformation weights with no additional regularization. Importantly, these weights are computed in real-time for any surface point, unlike prior methods which require expensive optimization. We introduce barycentric feature distillation, an improved feature distillation pipeline which leverages the full visual signal from shape renders to make distillation complexity robust to mesh resolution. This enables high resolution meshes to be processed in minutes versus potentially hours for prior methods. We preserve and extend classical properties through feature space constraints and locality weighting. Our field representation enables automatic visual symmetry detection, which we use to produce symmetry-preserving deformations. We show a proof-of-concept application which can produce deformations for meshes up to 1 million faces in real-time on a consumer-grade machine. Project page at https://threedle.github.io/dfd.

翻译：基于手柄的网格形变是计算机图形学中的经典范式，能够通过稀疏控制实现直观编辑。经典技术虽然快速精准，但要求用户预知理想手柄放置位置，这一过程往往缺乏直观性且难以保持一致性。由于权重通常通过手柄定义的能量函数优化，手柄集无法轻易调整。现代数据驱动方法虽能提供语义化编辑，却牺牲了精细控制能力与运行速度。本文提出一种兼得两者优势的技术：通过深度特征邻近性生成平滑且具有视觉感知能力的形变权重，无需额外正则化。与依赖昂贵优化过程的先前方法不同，本方法可对任意表面点实时计算权重。我们引入重心特征蒸馏——一种改进的特征蒸馏流程，通过充分利用形状渲染中的完整视觉信号，使蒸馏复杂度对网格分辨率具有鲁棒性。这使得高分辨率网格的处理时间从先前方法的数小时缩短至数分钟。通过特征空间约束与局部加权，我们保留并扩展了经典属性。该场表示方法支持自动视觉对称性检测，并据此生成保对称形变。我们展示了概念验证应用，可在消费级机器上对百万面片量级的网格实现实时形变。项目页面：https://threedle.github.io/dfd。