Grounding Free-Form Instructions for Fashion Complementary Image Generation

Matteo Attimonelli; Claudio Pomo; Alessandro De Bellis; Danilo Danese; Dietmar Jannach; Tommaso Di Noia

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arXiv · 2608.23302

Grounding Free-Form Instructions for Fashion Complementary Image Generation

Matteo Attimonelli·Claudio Pomo·Alessandro De Bellis·Danilo Danese·Dietmar Jannach·Tommaso Di Noia

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Abstract

Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural multimodal grounding problem where models must interpret language in visual context. Existing CIG benchmarks rely on rigid template prompts (e.g., "a photo of a skirt"), failing to reflect natural user queries and obscuring model behavior across levels of linguistic specificity. We introduce fashion complementary image generation with free-form instructions, a multimodal language-grounding setting where a model generates a compatible garment from a seed image and a natural-language instruction. To this end, we enrich three CIG benchmarks with low-, medium-, and high-specificity instructions generated by a vision-language model and validated by human annotators. We instantiate the task with StyleFlow, a Rectified Flow Matching model that jointly conditions on the seed image and instruction within a single multimodal transformer. Across image quality metrics, catalog-alignment analysis, ablations, and human evaluation, StyleFlow consistently produces instruction-aligned and stylistically coherent garments while reducing architectural complexity and inference cost relative to auxiliary-module approaches.

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Matteo Attimonelli, Claudio Pomo, Alessandro De Bellis, Danilo Danese, Dietmar Jannach, Tommaso Di Noia. 2026-08-24. Grounding Free-Form Instructions for Fashion Complementary Image Generation. https://arxiv.org/abs/2608.23302

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