GarmentSketch: Large-scale Sketch-to-Fashion Benchmark

• URL: http://arxiv.org/abs/2606.14025v1
• Date: Fri, 12 Jun 2026 01:58:35 GMT
• Title: GarmentSketch: Large-scale Sketch-to-Fashion Benchmark
• Abstract summary: GarmentSketch is a novel dataset comprising 26,249 fashion sketches across 21 garment categories, each paired with detailed textual descriptions.<n>We benchmark GarmentSketch on state-of-the-art generative models, providing baseline performance for sketch-guided text-to-image generation.<n>By offering a comprehensive and richly annotated resource, GarmentSketch establishes a foundation for advancing sketch understanding, fine-grained fashion image generation, and creative human-AI collaboration in design.
• Score: 21.93639380627745
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fashion sketching is a cornerstone of design workflows, allowing rapid visualization of creative concepts prior to physical prototyping. Yet, progress in sketch-based fashion image synthesis has been hindered by the absence of large-scale, high-quality paired resources. To bridge this gap, we present GarmentSketch, a novel dataset comprising 26,249 fashion sketches across 21 garment categories, each paired with detailed textual descriptions. Captions were produced through a multi-stage pipeline that integrates multiple multimodal large language models (MLLMs) with human-in-the-loop refinement, ensuring both semantic accuracy and descriptive richness. We benchmark GarmentSketch on state-of-the-art generative models, providing baseline performance for sketch-guided text-to-image generation. Our experiments reveal both the promise and the current limitations of existing methods. By offering a comprehensive and richly annotated resource, GarmentSketch establishes a foundation for advancing sketch understanding, fine-grained fashion image generation, and creative human-AI collaboration in design. The dataset will be available at: https://khangbdd.github.io/garmentsketch.

Related papers

• VideoSketcher: Video Models Prior Enable Versatile Sequential Sketch Generation [73.23035143627598]
  Most generative models treat sketches as static images, overlooking the temporal structure that underlies creative drawing.<n>We present a data-efficient approach for sequential sketch generation that adapts pretrained text-to-video diffusion models.<n>Our method generates high-quality sketches that closely follow text-specified orderings while exhibiting rich visual detail.
  arXiv  Detail & Related papers  (2026-02-17T18:55:03Z)
• LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing [13.90016469666642]
  We present LOcalized Text and Sketch for fashion image generation (LOTS), an approach for compositional sketch-text based generation of complete fashion outlooks.<n>LOTS leverages a global description with paired localized sketch + text information for conditioning and introduces a novel step-based merging strategy for diffusion adaptation.<n>To validate our method, we build on Fashionpedia to release Sketchy, the first fashion dataset where multiple text-sketch pairs are provided per image.
  arXiv  Detail & Related papers  (2025-07-30T12:48:29Z)
• HiGarment: Cross-modal Harmony Based Diffusion Model for Flat Sketch to Realistic Garment Image [20.177936034245572]
  HiGarment is a novel framework that enhances fabric representation across textual and visual modalities.<n>We present Flat Sketch to Realistic Garment Image (FS2RG), which generates realistic garment images by integrating flat sketches and textual guidance.<n>We collect Multi-modal Detailed Garment, the largest open-source dataset for garment generation.
  arXiv  Detail & Related papers  (2025-05-29T07:23:40Z)
• AIpparel: A Multimodal Foundation Model for Digital Garments [71.12933771326279]
  We introduce AIpparel, a multimodal foundation model for generating and editing sewing patterns.<n>Our model fine-tunes state-of-the-art large multimodal models on a custom-curated large-scale dataset of over 120,000 unique garments.<n>We propose a novel tokenization scheme that concisely encodes these complex sewing patterns so that LLMs can learn to predict them efficiently.
  arXiv  Detail & Related papers  (2024-12-05T07:35:19Z)
• HAIFIT: Human-to-AI Fashion Image Translation [6.034505799418777]
  We introduce HAIFIT, a novel approach that transforms sketches into high-fidelity, lifelike clothing images. Our method excels in preserving the distinctive style and intricate details essential for fashion design applications.
  arXiv  Detail & Related papers  (2024-03-13T16:06:07Z)
• Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing [40.70752781891058]
  We propose the task of multimodal-conditioned fashion image editing, guiding the generation of human-centric fashion images. We tackle this problem by proposing a new architecture based on latent diffusion models. Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets.
  arXiv  Detail & Related papers  (2023-04-04T18:03:04Z)
• FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning [66.38951790650887]
  Multimodal tasks in the fashion domain have significant potential for e-commerce. We propose a novel fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs. We show the triplet-based tasks are an effective addition to standard multimodal pre-training tasks.
  arXiv  Detail & Related papers  (2022-10-26T21:01:19Z)
• ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design [66.68194916359309]
  Cross-modal fashion image synthesis has emerged as one of the most promising directions in the generation domain. MaskCLIP decomposes the garments into semantic parts, ensuring fine-grained and semantically accurate alignment between the visual and text information. ArmANI discretizes an image into uniform tokens based on a learned cross-modal codebook in its first stage and uses a Transformer to model the distribution of image tokens for a real image.
  arXiv  Detail & Related papers  (2022-08-11T03:44:02Z)
• On Learning Semantic Representations for Million-Scale Free-Hand Sketches [146.52892067335128]
  We study learning semantic representations for million-scale free-hand sketches. We propose a dual-branch CNNRNN network architecture to represent sketches. We explore learning the sketch-oriented semantic representations in hashing retrieval and zero-shot recognition.
  arXiv  Detail & Related papers  (2020-07-07T15:23:22Z)
• SketchyCOCO: Image Generation from Freehand Scene Sketches [71.85577739612579]
  We introduce the first method for automatic image generation from scene-level freehand sketches. Key contribution is an attribute vector bridged Geneversarative Adrial Network called EdgeGAN. We have built a large-scale composite dataset called SketchyCOCO to support and evaluate the solution.
  arXiv  Detail & Related papers  (2020-03-05T14:54:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.