# InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging

arXiv: https://arxiv.org/abs/2604.02764 · HTML: https://mlsanity.com/paper/2604.02764/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 65 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 30 of 43)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [Sapiens: Foundation for Human Vision Models](https://mlsanity.com/paper/2408.12569/) (2024)
- [Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation](https://mlsanity.com/paper/2306.17115/) (2023)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers](https://mlsanity.com/paper/2406.10163/) (2024)
- [ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering](https://mlsanity.com/paper/2312.05941/) (2023)
- [MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization](https://mlsanity.com/paper/2408.02555/) (2024)
- [Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images](https://mlsanity.com/paper/2003.12753/) (2020)
- [The Power of Points for Modeling Humans in Clothing](https://mlsanity.com/paper/2109.01137/) (2021)
- [Learning-Based Animation of Clothing for Virtual Try-On](https://mlsanity.com/paper/1903.07190/) (2019)
- [Scaling Mesh Generation via Compressive Tokenization](https://mlsanity.com/paper/2411.07025/) (2024)
- [DeepCloth: Neural Garment Representation for Shape and Style Editing](https://mlsanity.com/paper/2011.14619/) (2020)
- [Learning a Shared Shape Space for Multimodal Garment Design](https://mlsanity.com/paper/1806.11335/) (2018)
- [PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations](https://mlsanity.com/paper/2404.04421/) (2024)
- [DressCode: Autoregressively Sewing and Generating Garments from Text Guidance](https://mlsanity.com/paper/2401.16465/) (2024)
- [DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation](https://mlsanity.com/paper/2311.12194/) (2023)
- [Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Images](https://mlsanity.com/paper/2203.15007/) (2022)
- [Point-Based Modeling of Human Clothing](https://mlsanity.com/paper/2104.08230/) (2021)
- [Generating Datasets of 3D Garments with Sewing Patterns](https://mlsanity.com/paper/2109.05633/) (2021)
- [Towards Garment Sewing Pattern Reconstruction from a Single Image](https://mlsanity.com/paper/2311.04218/) (2023)
- [ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns](https://mlsanity.com/paper/2305.14100/) (2023)
- [REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos](https://mlsanity.com/paper/2305.14236/) (2023)
- [GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns](https://mlsanity.com/paper/2405.17609/) (2024)
- [Garment3DGen: 3D Garment Stylization and Texture Generation](https://mlsanity.com/paper/2403.18816/) (2024)
- [GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond](https://mlsanity.com/paper/2403.19632/) (2024)

## Built on by (most cited first, 1 of 1)

- [Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns](https://mlsanity.com/paper/2607.00829/) (2026)

## Abstract (arXiv)

Recovering sewing patterns from draped 3D garments is a challenging problem in human digitization research. In contrast to the well-studied forward process of draping designed sewing patterns using mature physical simulation engines, the inverse process of recovering parametric 2D patterns from deformed garment geometry remains fundamentally ill-posed for existing methods. We propose a two-stage framework that centers on a structured intermediate representation, BoxMesh, which serves as the key to bridging the gap between 3D garment geometry and parametric sewing patterns. BoxMesh encodes both garment-level geometry and panel-level structure in 3D, while explicitly disentangling intrinsic panel geometry and stitching topology from draping-induced deformations. This representation imposes a physically grounded structure on the problem, significantly reducing ambiguity. In Stage I, a geometry-driven autoregressive model infers BoxMesh from the input 3D garment. In Stage II, a semantics-aware autoregressive model parses BoxMesh into parametric sewing patterns. We adopt autoregressive modeling to naturally handle the variable-length and structured nature of panel configurations and stitching relationships. This decomposition separates geometric inversion from structured pattern inference, leading to more accurate and robust recovery. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the GarmentCodeData benchmark and generalizes effectively to real-world scans and single-view images.