DressWild: Feed-Forward Pose-Agnostic Garment Sewing Pattern Generation from In-the-Wild Images

TL;DR

DressWild is proposed, a novel feed-forward pipeline that reconstructs physics-consistent 2D sewing patterns and the corresponding 3D garments from in-the-wild images without requiring multi-view inputs or iterative optimization, offering an efficient and scalable solution for realistic garment simulation and animation.

Abstract

Recent advances in garment pattern generation have shown promising progress. However, existing feed-forward methods struggle with diverse poses and viewpoints, while optimization-based approaches are computationally expensive and difficult to scale. This paper focuses on sewing pattern generation for garment modeling and fabrication applications that demand editable, separable, and simulation-ready garments. We propose DressWild, a novel feed-forward pipeline that reconstructs physics-consistent 2D sewing patterns and the corresponding 3D garments from a single in-the-wild image. Given an input image, our method leverages vision-language models (VLMs) to normalize pose variations at the image level, then extract pose-aware, 3D-informed garment features. These features are fused through a transformer-based encoder and subsequently used to predict sewing pattern parameters, which can be directly applied to physical simulation, texture synthesis, and multi-layer virtual try-on. Extensive experiments demonstrate that our approach robustly recovers diverse sewing patterns and the corresponding 3D garments from in-the-wild images without requiring multi-view inputs or iterative optimization, offering an efficient and scalable solution for realistic garment simulation and animation.

Figures (6)

• Figure 1: Overview of our pipeline. Given a single in-the-wild image, our method reconstructs simulation-ready sewing patterns and a corresponding 3D garment.
• Figure 2: Data Curation and Augmentation. We use VLM to generate novel pose and novel view images with the consistent garment.
• Figure 3: Qualitative Comparison. Our method produces high-quality 2D sewing patterns and corresponding 3D garments. Here we qualitatively compare our results with baseline approaches SewFormer sewformer and NeuralTailor korosteleva2022neuraltailor.
• Figure 4: Results from in-the-wild image with multi-layer garments. Given an in-the-wild image as input, our DressWild method can generate multi-layer pose-agnostic and simulation-ready sewing patterns.
• Figure 5: Ablation of pose features. The model identifies the garment better with the assistance of the pose feature when the human pose is challenging.