# SENC: Handling Self-collision in Neural Cloth Simulation

arXiv: https://arxiv.org/abs/2407.12479 · HTML: https://mlsanity.com/paper/2407.12479/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 60 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Learning to Simulate Complex Physics with Graph Networks](https://mlsanity.com/paper/2002.09405/) (2020)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [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)
- [DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact](https://mlsanity.com/paper/2106.05306/) (2021)
- [PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation](https://mlsanity.com/paper/2012.11310/) (2020)
- [DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation](https://mlsanity.com/paper/2009.02715/) (2020)
- [Neural Cloth Simulation](https://mlsanity.com/paper/2212.11220/) (2022)
- [Codimensional Incremental Potential Contact](https://mlsanity.com/paper/2012.04457/) (2020)
- [Disentangled Clothed Avatar Generation from Text Descriptions](https://mlsanity.com/paper/2312.05295/) (2023)
- [ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment Simulations](https://mlsanity.com/paper/2405.09522/) (2024)
- [CaPhy: Capturing Physical Properties for Animatable Human Avatars](https://mlsanity.com/paper/2308.05925/) (2023)
- [A Repulsive Force Unit for Garment Collision Handling in Neural Networks](https://mlsanity.com/paper/2207.13871/) (2022)
- [ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes](https://mlsanity.com/paper/2304.03492/) (2023)
- [A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detection](https://mlsanity.com/paper/2009.13349/) (2020)

## Built on by (most cited first, 4 of 4)

- [WorldParticle: Unified World Simulation of Lagrangian Particle Dynamics via Transformer](https://mlsanity.com/paper/2605.15305/) (2026)
- [ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation](https://mlsanity.com/paper/2605.27852/) (2026)
- [PhysDrape: Learning Explicit Forces and Collision Constraints for Physically Realistic Garment Draping](https://mlsanity.com/paper/2602.08020/) (2026)
- [MeshPriorDiT: Hierarchical Modeling for Action-Conditioned Cloth Dynamics](https://mlsanity.com/paper/2608.26766/) (2026)

## Abstract (arXiv)

We present SENC, a novel self-supervised neural cloth simulator that addresses the challenge of cloth self-collision. This problem has remained unresolved due to the gap in simulation setup between recent collision detection and response approaches and self-supervised neural simulators. The former requires collision-free initial setups, while the latter necessitates random cloth instantiation during training. To tackle this issue, we propose a novel loss based on Global Intersection Analysis (GIA). This loss extracts the volume surrounded by the cloth region that forms the penetration. By constructing an energy based on this volume, our self-supervised neural simulator can effectively address cloth self-collisions. Moreover, we develop a self-collision-aware graph neural network capable of learning to handle self-collisions, even for parts that are topologically distant from one another. Additionally, we introduce an effective external force scheme that enables the simulation to learn the cloth's behavior in response to random external forces. We validate the efficacy of SENC through extensive quantitative and qualitative experiments, demonstrating that it effectively reduces cloth self-collision while maintaining high-quality animation results.