# Deep Detail Enhancement for Any Garment

arXiv: https://arxiv.org/abs/2008.04367 · HTML: https://mlsanity.com/paper/2008.04367/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 22 of 22)

- [Real-time Deep Dynamic Characters](https://mlsanity.com/paper/2105.01794/) (2021)
- [Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing](https://mlsanity.com/paper/2108.08807/) (2021)
- [Modeling Clothing as a Separate Layer for an Animatable Human Avatar](https://mlsanity.com/paper/2106.14879/) (2021)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing](https://mlsanity.com/paper/2206.15470/) (2022)
- [Motion Guided Deep Dynamic 3D Garments](https://mlsanity.com/paper/2209.11449/) (2022)
- [Structure-Preserving 3D Garment Modeling with Neural Sewing Machines](https://mlsanity.com/paper/2211.06701/) (2022)
- [N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks](https://mlsanity.com/paper/2112.06397/) (2021)
- [Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images](https://mlsanity.com/paper/2303.08639/) (2023)
- [Neural Garment Dynamics via Manifold-Aware Transformers](https://mlsanity.com/paper/2407.06101/) (2024)
- [ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes](https://mlsanity.com/paper/2304.03492/) (2023)
- [Deep Deformation Detail Synthesis for Thin Shell Models](https://mlsanity.com/paper/2102.11541/) (2021)
- [PhysGraph: Physics-Based Integration Using Graph Neural Networks](https://mlsanity.com/paper/2301.11841/) (2023)
- [A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping](https://mlsanity.com/paper/2311.02700/) (2023)
- [SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging Garments](https://mlsanity.com/paper/2308.02827/) (2023)
- [Detail-aware Deep Clothing Animations Infused with Multi-source Attributes](https://mlsanity.com/paper/2112.07974/) (2021)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [PhysXNet: A Customizable Approach for LearningCloth Dynamics on Dressed People](https://mlsanity.com/paper/2111.07195/) (2021)
- [Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting](https://mlsanity.com/paper/2401.03108/) (2024)
- [Neural Garment Dynamic Super-Resolution](https://mlsanity.com/paper/2412.06285/) (2024)
- [Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution](https://mlsanity.com/paper/2305.03216/) (2023)
- [Learning High-Fidelity Cloth Animation via Skinning-Free Image Transfer](https://mlsanity.com/paper/2512.05593/) (2025)

## Abstract (arXiv)

Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low-resolution physically-based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data-driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch-based formulation, that produces high-resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light-weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion.