# IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design

arXiv: https://arxiv.org/abs/2504.13176 · HTML: https://mlsanity.com/paper/2504.13176/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 69 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 30 of 47)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [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)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Sketch-Guided Text-to-Image Diffusion Models](https://mlsanity.com/paper/2211.13752/) (2022)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://mlsanity.com/paper/2403.01779/) (2024)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Learning to generate line drawings that convey geometry and semantics](https://mlsanity.com/paper/2203.12691/) (2022)
- [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https://mlsanity.com/paper/1801.02753/) (2018)
- [Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation](https://mlsanity.com/paper/1909.11081/) (2019)
- [User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks](https://mlsanity.com/paper/1808.03240/) (2018)

## Built on by (most cited first, 30 of 36)

- [Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization](https://mlsanity.com/paper/2506.15980/) (2025)
- [R-Genie: Reasoning-Guided Generative Image Editing](https://mlsanity.com/paper/2505.17768/) (2025)
- [StyleTailor: Towards Personalized Fashion Styling via Hierarchical Negative Feedback](https://mlsanity.com/paper/2508.06555/) (2025)
- [FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation](https://mlsanity.com/paper/2505.17544/) (2025)
- [MCFNet: A Multimodal Collaborative Fusion Network for Fine-Grained Semantic Classification](https://mlsanity.com/paper/2505.23365/) (2025)
- [YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection](https://mlsanity.com/paper/2506.21135/) (2025)
- [RefAdGen: High-Fidelity Advertising Image Generation](https://mlsanity.com/paper/2508.11695/) (2025)
- [LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection](https://mlsanity.com/paper/2607.25962/) (2026)
- [Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection](https://mlsanity.com/paper/2505.17683/) (2025)
- [YOLO-SPCI: Enhancing Remote Sensing Object Detection via Selective-Perspective-Class Integration](https://mlsanity.com/paper/2505.21370/) (2025)
- [ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2505.24481/) (2025)
- [O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentation](https://mlsanity.com/paper/2506.11913/) (2025)
- [DGE-YOLO: Dual-Branch Gathering and Attention for Accurate UAV Object Detection](https://mlsanity.com/paper/2506.23252/) (2025)
- [DiffFit: Disentangled Garment Warping and Texture Refinement for Virtual Try-On](https://mlsanity.com/paper/2506.23295/) (2025)
- [OptiPrune: Boosting Prompt-Image Consistency with Attention-Guided Noise and Dynamic Token Selection](https://mlsanity.com/paper/2507.00789/) (2025)
- [Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction](https://mlsanity.com/paper/2508.02043/) (2025)
- [PQ-DAF: Pose-driven Quality-controlled Data Augmentation for Data-scarce Driver Distraction Detection](https://mlsanity.com/paper/2508.10397/) (2025)
- [Animalbooth: multimodal feature enhancement for animal subject personalization](https://mlsanity.com/paper/2509.16702/) (2025)
- [Instruction Guided Multi Object Image Editing with Quantity and Layout Consistency](https://mlsanity.com/paper/2509.24514/) (2025)
- [Span-level Detection of AI-generated Scientific Text via Contrastive Learning and Structural Calibration](https://mlsanity.com/paper/2510.00890/) (2025)
- [GCA-ResUNet: Medical Image Segmentation Using Grouped Coordinate Attention](https://mlsanity.com/paper/2512.23990/) (2025)
- [Interpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuning](https://mlsanity.com/paper/2602.03530/) (2026)
- [Causal Discovery for Cross-Sectional Data Based on Super-Structure and Divide-and-Conquer](https://mlsanity.com/paper/2602.03914/) (2026)
- [FGAA-FPN: Foreground-Guided Angle-Aware Feature Pyramid Network for Oriented Object Detection](https://mlsanity.com/paper/2602.10710/) (2026)
- [Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation](https://mlsanity.com/paper/2602.13263/) (2026)
- [RSTNet: Enhancing Small-Target Recognition in Noisy SAR Imagery via Robust Feature Learning and Distribution-Aware Regression](https://mlsanity.com/paper/2602.23820/) (2026)
- [RMK RetinaNet: Rotated Multi-Kernel RetinaNet for Robust Oriented Object Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2603.04793/) (2026)
- [BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off](https://mlsanity.com/paper/2603.09236/) (2026)
- [Multi-Period Texture Contrast Enhancement for Low-Contrast Wafer Defect Detection and Segmentation](https://mlsanity.com/paper/2603.14282/) (2026)
- [SARES-DEIM: Sparse Mixture-of-Experts Meets DETR for Robust SAR Ship Detection](https://mlsanity.com/paper/2604.04127/) (2026)

## Abstract (arXiv)

This paper presents IMAGGarment, a fine-grained garment generation (FGG) framework that enables high-fidelity garment synthesis with precise control over silhouette, color, and logo placement. Unlike existing methods that are limited to single-condition inputs, IMAGGarment addresses the challenges of multi-conditional controllability in personalized fashion design and digital apparel applications. Specifically, IMAGGarment employs a two-stage training strategy to separately model global appearance and local details, while enabling unified and controllable generation through end-to-end inference. In the first stage, we propose a global appearance model that jointly encodes silhouette and color using a mixed attention module and a color adapter. In the second stage, we present a local enhancement model with an adaptive appearance-aware module to inject user-defined logos and spatial constraints, enabling accurate placement and visual consistency. To support this task, we release GarmentBench, a large-scale dataset comprising over 180K garment samples paired with multi-level design conditions, including sketches, color references, logo placements, and textual prompts. Extensive experiments demonstrate that our method outperforms existing baselines, achieving superior structural stability, color fidelity, and local controllability performance. Code, models, and datasets are publicly available at https://github.com/muzishen/IMAGGarment.