# GAPS: Geometry-Aware, Physics-Based, Self-Supervised Neural Garment Draping

arXiv: https://arxiv.org/abs/2312.01490 · HTML: https://mlsanity.com/paper/2312.01490/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 3 Dec 2023.

## Builds on (most cited first, 26 of 26)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation](https://mlsanity.com/paper/1808.05942/) (2018)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (2021)
- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence](https://mlsanity.com/paper/2003.14286/) (2020)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [DeepWrinkles: Accurate and Realistic Clothing Modeling](https://mlsanity.com/paper/1808.03417/) (2018)
- [Learning-Based Animation of Clothing for Virtual Try-On](https://mlsanity.com/paper/1903.07190/) (2019)
- [SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing](https://mlsanity.com/paper/2007.11610/) (2020)
- [SNUG: Self-Supervised Neural Dynamic Garments](https://mlsanity.com/paper/2204.02219/) (2022)
- [Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On](https://mlsanity.com/paper/2105.06462/) (2021)
- [GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping](https://mlsanity.com/paper/1811.10983/) (2018)
- [3D Face Reconstruction by Learning from Synthetic Data](https://mlsanity.com/paper/1609.04387/) (2016)
- [PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation](https://mlsanity.com/paper/2012.11310/) (2020)
- [Shape Reconstruction by Learning Differentiable Surface Representations](https://mlsanity.com/paper/1911.11227/) (2019)
- [Neural Cloth Simulation](https://mlsanity.com/paper/2212.11220/) (2022)
- [Neural Point-based Shape Modeling of Humans in Challenging Clothing](https://mlsanity.com/paper/2209.06814/) (2022)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss](https://mlsanity.com/paper/2007.10867/) (2020)
- [Robust Isometric Non-Rigid Structure-from-Motion](https://mlsanity.com/paper/2010.04690/) (2020)
- [Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases](https://mlsanity.com/paper/2104.06950/) (2021)

## Built on by (most cited first, 9 of 9)

- [D-Garment: Physically Grounded Latent Diffusion for Dynamic Garment Deformations](https://mlsanity.com/paper/2504.03468/) (2025)
- [NGD: Neural Gradient Based Deformation for Monocular Garment Reconstruction](https://mlsanity.com/paper/2508.17712/) (2025)
- [Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints](https://mlsanity.com/paper/2507.22699/) (2025)
- [Learning High-Fidelity Cloth Animation via Skinning-Free Image Transfer](https://mlsanity.com/paper/2512.05593/) (2025)
- [FNOPT: Resolution-Agnostic, Self-Supervised Cloth Simulation using Meta-Optimization with Fourier Neural Operators](https://mlsanity.com/paper/2512.05762/) (2025)
- [Patch-based Representation and Learning for Efficient Deformation Modeling](https://mlsanity.com/paper/2601.05035/) (2026)
- [PhysDrape: Learning Explicit Forces and Collision Constraints for Physically Realistic Garment Draping](https://mlsanity.com/paper/2602.08020/) (2026)
- [HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning](https://mlsanity.com/paper/2605.20460/) (2026)
- [DiT-Garment: Garment Dynamics with Diffusion Transformers](https://mlsanity.com/paper/2609.18510/) (2026)

## Abstract (arXiv)

Recent neural, physics-based modeling of garment deformations allows faster and visually aesthetic results as opposed to the existing methods. Material-specific parameters are used by the formulation to control the garment inextensibility. This delivers unrealistic results with physically implausible stretching. Oftentimes, the draped garment is pushed inside the body which is either corrected by an expensive post-processing, thus adding to further inconsistent stretching; or by deploying a separate training regime for each body type, restricting its scalability. Additionally, the flawed skinning process deployed by existing methods produces incorrect results on loose garments. In this paper, we introduce a geometrical constraint to the existing formulation that is collision-aware and imposes garment inextensibility wherever possible. Thus, we obtain realistic results where draped clothes stretch only while covering bigger body regions. Furthermore, we propose a geometry-aware garment skinning method by defining a body-garment closeness measure which works for all garment types, especially the loose ones.