# TrendGen: An Outfit Recommendation and Display System

arXiv: https://arxiv.org/abs/2603.27264 · HTML: https://mlsanity.com/paper/2603.27264/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 59 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On](https://mlsanity.com/paper/2312.01725/) (2023)
- [OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://mlsanity.com/paper/2403.01779/) (2024)
- [Improving Diffusion Models for Authentic Virtual Try-on in the Wild](https://mlsanity.com/paper/2403.05139/) (2024)
- [AnglE-optimized Text Embeddings](https://mlsanity.com/paper/2309.12871/) (2023)
- [Learning Fashion Compatibility with Bidirectional LSTMs](https://mlsanity.com/paper/1707.05691/) (2017)
- [Multi-Modal Classifiers for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2306.05493/) (2023)
- [Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/2402.18078/) (2024)
- [Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences](https://mlsanity.com/paper/1509.07473/) (2015)
- [OutfitTransformer: Learning Outfit Representations for Fashion Recommendation](https://mlsanity.com/paper/2204.04812/) (2022)
- [SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions](https://mlsanity.com/paper/1604.04835/) (2016)
- [TryOffAnyone: Tiled Cloth Generation from a Dressed Person](https://mlsanity.com/paper/2412.08573/) (2024)
- [Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval](https://mlsanity.com/paper/2009.09809/) (2020)
- [Image and Encoded Text Fusion for Multi-Modal Classification](https://mlsanity.com/paper/1810.02001/) (2018)
- [RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation](https://mlsanity.com/paper/2411.19528/) (2024)
- [MGT: Extending Virtual Try-Off to Multi-Garment Scenarios](https://mlsanity.com/paper/2504.13078/) (2025)
- [IGR: Improving Diffusion Model for Garment Restoration from Person Image](https://mlsanity.com/paper/2412.11513/) (2024)

## Abstract (arXiv)

Recent advances in Computer Vision have significantly improved image understanding and generation, revolutionizing the fashion industry. However, challenges such as inconsistent lighting, non-ideal garment angles, complex backgrounds, and occlusions in raw images hinder their full potential. Overcoming these obstacles is crucial for developing robust fashion AI systems capable of real-world applications. In this paper, we introduce TrendGen, a Fashion AI system designed to enhance online shopping with intelligent outfit recommendations. Deployed on a major e-commerce platform, TrendGen leverages cloth images and product attributes to generate trend-aligned, cohesive outfit suggestions. Additionally, it employs Generative AI to transform raw images into high-quality lay-down views, offering a clear and structured presentation of garments. Our evaluation on production data demonstrates TrendGen's consistent high-quality outfits and lay-down images, marking a significant advancement in AI-driven solutions for fashion retail.