Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting
arXiv:2101.08301 · doi:10.1007/s00530-026-02232-x
Abstract
Fashion-focused artificial intelligence has rapidly advanced in recent years, driven by deep learning and its deployment in recommender systems, detection, retrieval, and analytics. Yet several consumer-facing domains remain comparatively under-surveyed despite their practical impact. This work provides a comprehensive review of methods, datasets, and evaluation metrics across four such domains: aesthetics, personalization, virtual try-on, and forecasting. We synthesize technical approaches spanning representation learning, preference modeling, image transformation, and time-series analysis; relate them to downstream recommender systems and user experience; and highlight cross-domain dependencies (e.g., aesthetics-informed personalization, trend-informed recommendations). We also catalog commonly used datasets and metrics, including those from object detection and image segmentation pipelines, where relevant to try-on and visual understanding. Finally, we identify open challenges and promising directions for integrated AI-driven fashion systems.
References in corpus (11)
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Learning Fashion Compatibility with Bidirectional LSTMs
- Mode Regularized Generative Adversarial Networks
- Clothing Co-Parsing by Joint Image Segmentation and Labeling
- C-VTON: Context-Driven Image-Based Virtual Try-On Network
- Visually Explainable Recommendation
- Diffusion Models for Generative Outfit Recommendation
- Visually-Aware Fashion Recommendation and Design with Generative Image Models
- Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends
- Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
- Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study