Deep Feature Deformation Weights

TL;DR

This paper introduces a real-time, data-driven mesh deformation technique that combines classical control with semantic understanding, enabling intuitive, high-resolution shape edits efficiently on consumer hardware.

Contribution

It presents barycentric feature distillation for robust, fast computation of deformation weights, integrating visual signals and symmetry detection for high-resolution mesh editing.

Findings

Real-time deformation weights for meshes up to 1 million faces.

Processing high-resolution meshes in minutes on consumer hardware.

Preservation of classical properties through feature space constraints.

Abstract

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.