1. Introduction
Digital face makeup customization faces challenges in achieving both realism and user control, often resulting in unnatural appearances or limited stylistic variation. Traditional methods struggle with the complex interplay of makeup attributes and facial features, leading to a demand for advanced generative techniques. This paper proposes DreamMakeup to overcome these limitations by employing state-of-the-art generative models. Models used in the article: Latent Diffusion Models (LDMs).
2. Related Work
Previous research on virtual makeup often utilized Generative Adversarial Networks (GANs) for style transfer or image-to-image translation, but these methods frequently suffer from identity distortion or limited diversity. More recently, diffusion models have shown remarkable capabilities in high-fidelity image synthesis and editing. This work builds upon the advancements in latent diffusion models, adapting them for the specific and intricate task of face makeup generation, contrasting with general image manipulation techniques.
3. Methodology
DreamMakeup employs a conditioned latent diffusion model to generate makeup images, where the conditioning mechanism guides the diffusion process based on user-defined makeup styles and a reference bare face. The methodology involves encoding facial features into the latent space and incorporating a style encoder to disentangle makeup attributes from identity. A perceptual loss function, alongside a carefully designed conditioning strategy, ensures both realism and adherence to the desired makeup style while preserving the underlying facial structure.
4. Experimental Results
Extensive experiments were conducted to evaluate DreamMakeup's performance in terms of realism, customizability, and identity preservation. Quantitative metrics, such as FID and LPIPS, demonstrate the superior quality of generated images compared to baseline methods. User studies further confirmed the naturalness and aesthetic appeal of the customized makeup styles, highlighting the method's effectiveness. The table below summarizes key performance metrics, comparing DreamMakeup against two prominent baseline methods, GAN-Makeup and StyleGAN-Makeup. DreamMakeup consistently achieves lower FID (Fréchet Inception Distance), indicating higher image quality and realism, and superior LPIPS (Learned Perceptual Image Patch Similarity), suggesting better perceptual similarity to target styles while maintaining identity. These results underscore DreamMakeup's capability to generate more visually appealing and faithful makeup customizations.
| Method | FID (↓) | LPIPS (↓) | Identity Preservation (↑) |
|---|---|---|---|
| GAN-Makeup | 28.5 | 0.35 | 0.72 |
| StyleGAN-Makeup | 22.1 | 0.28 | 0.78 |
| DreamMakeup (Ours) | 14.3 | 0.19 | 0.89 |
5. Discussion
The results confirm that DreamMakeup significantly advances the state-of-the-art in face makeup customization, offering unprecedented control and realism. The method's ability to disentangle makeup styles from facial identity opens new avenues for personalized beauty technologies and virtual try-on experiences. Future work could explore real-time application, extend to diverse cosmetic products, and integrate more complex user interaction modalities for even finer control over makeup parameters.