Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework

2022-09-15

IEEE Transactions on Neural Networks and Learning Systems

AI Summary

This work addresses the challenging problem of complete outfit generation conditioned on a single garment and target-region masks—a key task in fashion design automation. We propose OutfitGAN, an end-to-end generative framework that synthesizes compatible tops, bottoms, footwear, and accessories given an input garment and spatially localized masks. Methodologically, we introduce two novel components: (i) a Semantic Alignment Module (SAM) that models fine-grained cross-garment semantic correspondences, and (ii) a Compatibility Classification Module (CCM) that explicitly enforces style and semantic coherence. Our multi-stage GAN architecture integrates semantic segmentation guidance, feature-level alignment losses, compatibility-aware adversarial supervision, and mask-conditioned generation control. Evaluated on a large-scale dataset of 20,000 real-world outfits, OutfitGAN achieves state-of-the-art performance across image fidelity, perceptual realism, and outfit compatibility metrics. It enables high-fidelity, diverse, and interactive fashion editing.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The field of fashion compatibility learning has attracted great attention from both the academic and industrial communities in recent years. Many studies have been carried out for fashion compatibility prediction, collocated outfit recommendation, artificial intelligence (AI)-enabled compatible fashion design, and related topics. In particular, AI-enabled compatible fashion design can be used to synthesize compatible fashion items or outfits to improve the design experience for designers or the efficacy of recommendations for customers. However, previous generative models for collocated fashion synthesis have generally focused on the image-to-image translation between fashion items of upper and lower clothing. In this article, we propose a novel outfit generation framework, i.e., OutfitGAN, with the aim of synthesizing a set of complementary items to compose an entire outfit, given one extant fashion item and reference masks of target synthesized items. OutfitGAN includes a semantic alignment module (SAM), which is responsible for characterizing the mapping correspondence between the existing fashion items and the synthesized ones, to improve the quality of the synthesized images, and a collocation classification module (CCM), which is used to improve the compatibility of a synthesized outfit. To evaluate the performance of our proposed models, we built a large-scale dataset consisting of 20 000 fashion outfits. Extensive experimental results on this dataset show that our OutfitGAN can synthesize photo-realistic outfits and outperform the state-of-the-art methods in terms of similarity, authenticity, and compatibility measurements.

Problem

• Synthesize compatible fashion items
• Improve outfit compatibility and design
• Generate entire outfits from single items

Innovation

• Semantic alignment module
• Collocation classification module
• OutfitGAN framework