Leveraging GANs For Active Appearance Models Optimized Model Fitting

TL;DR

This paper investigates integrating GANs with Active Appearance Models to improve fitting accuracy and convergence in complex, real-world image variations, demonstrating promising initial results on face alignment datasets.

Contribution

It introduces a GAN-augmented framework for AAM fitting using a U-Net generator and PatchGAN discriminator, addressing non-linear appearance variations and occlusions.

Findings

GAN-enhanced AAM achieves higher accuracy than classic methods.

Faster convergence in face alignment tasks.

Feasibility of GANs for deformable model fitting under challenging conditions.

Abstract

Active Appearance Models (AAMs) are a well-established technique for fitting deformable models to images, but they are limited by linear appearance assumptions and can struggle with complex variations. In this paper, we explore if the AAM fitting process can benefit from a Generative Adversarial Network (GAN). We uses a U-Net based generator and a PatchGAN discriminator for GAN-augmented framework in an attempt to refine the appearance model during fitting. This approach attempts to addresses challenges such as non-linear appearance variations and occlusions that traditional AAM optimization methods may fail to handle. Limited experiments on face alignment datasets demonstrate that the GAN-enhanced AAM can achieve higher accuracy and faster convergence than classic approaches with some manual interventions. These results establish feasibility of GANs as a tool for improving deformable model fitting in challenging conditions while maintaining efficient performance, and establishes the need for more future work to evaluate this approach at scale.