FLOWING: Implicit Neural Flows for Structure-Preserving Morphing

Arthur Bizzi, Matias Grynberg Portnoy, Vitor Pereira Matias, Daniel Perazzo, João Paulo Silva do Monte Lima, Luiz Velho, Nuno Gonçalves, João M. Pereira, Guilherme Schardong, Tiago Novello

Keywords: flows, implicit neural representations, morphing, warping

Abstract:

Morphing is a long-standing problem in vision and computer graphics, requir-
ing 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 prop-
erties 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 in
https://schardong.github.io/flowing.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 13820