# ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment Simulations

arXiv: https://arxiv.org/abs/2405.09522 · HTML: https://mlsanity.com/paper/2405.09522/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 12 of 12)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [Learning-Based Animation of Clothing for Virtual Try-On](https://mlsanity.com/paper/1903.07190/) (2019)
- [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)
- [HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics](https://mlsanity.com/paper/2212.07242/) (2022)
- [PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation](https://mlsanity.com/paper/2012.11310/) (2020)
- [Neural Cloth Simulation](https://mlsanity.com/paper/2212.11220/) (2022)
- [High-order Differentiable Autoencoder for Nonlinear Model Reduction](https://mlsanity.com/paper/2102.11026/) (2021)
- [Data-Free Learning of Reduced-Order Kinematics](https://mlsanity.com/paper/2305.03846/) (2023)
- [Towards Multi-Layered 3D Garments Animation](https://mlsanity.com/paper/2305.10418/) (2023)

## Built on by (most cited first, 23 of 23)

- [Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video](https://mlsanity.com/paper/2409.08189/) (2024)
- [ChatGarment: Garment Estimation, Generation and Editing via Large Language Models](https://mlsanity.com/paper/2412.17811/) (2024)
- [TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting](https://mlsanity.com/paper/2503.17032/) (2025)
- [PICA: Physics-Integrated Clothed Avatar](https://mlsanity.com/paper/2407.05324/) (2024)
- [MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics](https://mlsanity.com/paper/2510.01619/) (2025)
- [SENC: Handling Self-collision in Neural Cloth Simulation](https://mlsanity.com/paper/2407.12479/) (2024)
- [Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image](https://mlsanity.com/paper/2603.14772/) (2026)
- [ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation](https://mlsanity.com/paper/2605.27852/) (2026)
- [D-Garment: Physically Grounded Latent Diffusion for Dynamic Garment Deformations](https://mlsanity.com/paper/2504.03468/) (2025)
- [NGL: Natural Garment Language for Training-Free Sewing Pattern Estimation](https://mlsanity.com/paper/2602.20700/) (2026)
- [DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars](https://mlsanity.com/paper/2605.21001/) (2026)
- [Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network](https://mlsanity.com/paper/2601.15110/) (2026)
- [Learning Sewing Patterns via Latent Flow Matching of Implicit Fields](https://mlsanity.com/paper/2601.17740/) (2026)
- [InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging](https://mlsanity.com/paper/2604.02764/) (2026)
- [Extended Short- and Long-Range Mesh Learning for Fast and Generalized Garment Simulation](https://mlsanity.com/paper/2504.11763/) (2025)
- [ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers](https://mlsanity.com/paper/2508.01381/) (2025)
- [LUIVITON: Learned Universal Interoperable VIrtual Try-ON](https://mlsanity.com/paper/2509.05030/) (2025)
- [UNIC: Neural Garment Deformation Field for Real-time Clothed Character Animation](https://mlsanity.com/paper/2603.25580/) (2026)
- [Momentum-Conserving Graph Neural Networks for Deformable Objects](https://mlsanity.com/paper/2604.26097/) (2026)
- [HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning](https://mlsanity.com/paper/2605.20460/) (2026)
- [PoseShield: Neural Collision Fields for Human Self-Collision Resolution](https://mlsanity.com/paper/2606.29686/) (2026)
- [MeshPriorDiT: Hierarchical Modeling for Action-Conditioned Cloth Dynamics](https://mlsanity.com/paper/2608.26766/) (2026)
- [DiT-Garment: Garment Dynamics with Diffusion Transformers](https://mlsanity.com/paper/2609.18510/) (2026)

## Abstract (arXiv)

Learning-based approaches to cloth simulation have started to show their potential in recent years. However, handling collisions and intersections in neural simulations remains a largely unsolved problem. In this work, we present moniker, a learning-based solution for handling intersections in neural cloth simulations. Unlike conventional approaches that critically rely on intersection-free inputs, moniker robustly recovers from intersections introduced through missed collisions, self-penetrating bodies, or errors in manually designed multi-layer outfits. The technical core of moniker is a novel intersection contour loss that penalizes interpenetrations and encourages rapid resolution thereof. We integrate our intersection loss with a collision-avoiding repulsion objective into a neural cloth simulation method based on graph neural networks (GNNs). We demonstrate our method's ability across a challenging set of diverse multi-layer outfits under dynamic human motions. Our extensive analysis indicates that moniker significantly improves collision handling for learned simulation and produces visually compelling results.