# Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting

arXiv: https://arxiv.org/abs/2101.08301 · HTML: https://mlsanity.com/paper/2101.08301/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 115 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 30 of 35)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [Toward Characteristic-Preserving Image-based Virtual Try-On Network](https://mlsanity.com/paper/1807.07688/) (2018)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [Towards Photo-Realistic Virtual Try-On by Adaptively GeneratingleftrightarrowPreserving Image Content](https://mlsanity.com/paper/2003.05863/) (2020)
- [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://mlsanity.com/paper/1405.3531/) (2014)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [Towards Multi-pose Guided Virtual Try-on Network](https://mlsanity.com/paper/1902.11026/) (2019)
- [Learning Fashion Compatibility with Bidirectional LSTMs](https://mlsanity.com/paper/1707.05691/) (2017)
- [POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion](https://mlsanity.com/paper/1905.01866/) (2019)
- [Learning Type-Aware Embeddings for Fashion Compatibility](https://mlsanity.com/paper/1803.09196/) (2018)
- [3DPeople: Modeling the Geometry of Dressed Humans](https://mlsanity.com/paper/1904.04571/) (2019)
- [Visually-Aware Fashion Recommendation and Design with Generative Image Models](https://mlsanity.com/paper/1711.02231/) (2017)
- [Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences](https://mlsanity.com/paper/1509.07473/) (2015)
- [Fashion Meets Computer Vision: A Survey](https://mlsanity.com/paper/2003.13988/) (2020)
- [Hierarchical Fashion Graph Network for Personalized Outfit Recommendation](https://mlsanity.com/paper/2005.12566/) (2020)
- [Pixel-Level Domain Transfer](https://mlsanity.com/paper/1603.07442/) (2016)
- [Fashion Forward: Forecasting Visual Style in Fashion](https://mlsanity.com/paper/1705.06394/) (2017)
- [Creating Capsule Wardrobes from Fashion Images](https://mlsanity.com/paper/1712.02662/) (2017)
- [GeoStyle: Discovering Fashion Trends and Events](https://mlsanity.com/paper/1908.11412/) (2019)
- [Clothing Co-Parsing by Joint Image Segmentation and Labeling](https://mlsanity.com/paper/1502.00739/) (2015)
- [Learning the Latent "Look": Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images](https://mlsanity.com/paper/1707.03376/) (2017)
- [Knowledge Enhanced Neural Fashion Trend Forecasting](https://mlsanity.com/paper/2005.03297/) (2020)
- [Neural Compatibility Modeling with Attentive Knowledge Distillation](https://mlsanity.com/paper/1805.00313/) (2018)
- [Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data](https://mlsanity.com/paper/1608.03016/) (2016)
- [StreetStyle: Exploring world-wide clothing styles from millions of photos](https://mlsanity.com/paper/1706.01869/) (2017)
- [Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation](https://mlsanity.com/paper/1806.08977/) (2018)

## Built on by (most cited first, 4 of 4)

- [Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment](https://mlsanity.com/paper/2210.13005/) (2022)
- [GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling](https://mlsanity.com/paper/2210.14145/) (2022)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)

## Abstract (arXiv)

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.