用GAN增强外观模型，提升人脸对齐的精度与速度
Leveraging GANs For Active Appearance Models Optimized Model Fitting
- 用U-Net生成器和PatchGAN判别器构建GAN框架，优化外观建模
- 在人脸数据集上实现更高精度与更快收敛，优于传统方法
- 适合处理非线性变化和遮挡场景，适用于复杂条件下的对齐任务
主动外观模型（AAM）是拟合可变形模型到图像的经典方法，但受限于线性外观假设，难以应对复杂变化。本文探索将生成对抗网络（GAN）引入AAM拟合过程，采用基于U-Net的生成器和PatchGAN判别器构建增强框架，在拟合过程中优化外观模型。该方法旨在解决传统AAM在非线性外观变化和遮挡情况下的拟合失败问题。在人脸对齐数据集上的有限实验表明，改进后的GAN增强AAM在部分人工干预下，相比经典方法实现了更高的准确率和更快的收敛速度。结果验证了GAN作为提升复杂条件下可变形模型拟合性能的有效工具，同时表明需进一步开展大规模评估。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。