Protégé: Learn and Generate Basic Makeup Styles with Generative Adversarial Networks (GANs)
Makeup is no longer confined to physical application; people now use mobile apps to digitally apply makeup to their photos, which they then share on social media. However, while this shift has made makeup more accessible, designing diverse makeup styles tailored to individual faces remains a challenge. This challenge currently must still be done manually by humans. Existing systems, such as makeup recommendation engines and makeup transfer techniques, offer limitations in creating innovative makeups for different individuals "intuitively" -- significant user effort and knowledge needed and limited makeup options available in app. Our motivation is to address this challenge by proposing Prot\'eg\'e, a new makeup application, leveraging recent generative model -- GANs to learn and automatically generate makeup styles. This is a task that existing makeup applications (i.e., makeup recommendation systems using expert system and makeup transfer methods) are unable to perform. Extensive experiments has been conducted to demonstrate the capability of Prot\'eg\'e in learning and creating diverse makeups, providing a convenient and intuitive way, marking a significant leap in digital makeup technology!
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsSimilar Papers 제목 키워드 기반
FFHQ-Makeup: Paired Synthetic Makeup Dataset with Facial Consistency Across Multiple Styles
Paired bare-makeup facial images are essential for a wide range of beauty-related tasks, such as virtual try-on, facial privacy protection, and facial aesthetics analysis. However, collecting high-quality paired makeup d…
Text-to-Image GenerationVirtual Try-onMakeup-Guided Facial Privacy Protection via Untrained Neural Network Priors
Deep learning-based face recognition (FR) systems pose significant privacy risks by tracking users without their consent. While adversarial attacks can protect privacy, they often produce visible artifacts compromising u…
DecoderFace RecognitionFace VerificationLADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
We propose a local adversarial disentangling network (LADN) for facial makeup and de-makeup. Central to our method are multiple and overlapping local adversarial discriminators in a content-style disentangling network fo…
Style TransferDisentangled Makeup Transfer with Generative Adversarial Network
Facial makeup transfer is a widely-used technology that aims to transfer the makeup style from a reference face image to a non-makeup face. Existing literature leverage the adversarial loss so that the generated faces ar…
DecoderFacial Makeup TransferGenerative Adversarial NetworkVirtual Try-onAnchoring on Reality: Breaking the Pseudo-Target Ceiling in Makeup Transfer
Makeup transfer applies a reference cosmetic style to a source face while preserving its identity and geometry. However, this task is severely hindered by the lack of real paired training data. Current methods rely on ei…