# Towards Loose-Fitting Garment Animation via Generative Model of Deformation Decomposition

arXiv: https://arxiv.org/abs/2312.14619 · HTML: https://mlsanity.com/paper/2312.14619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (2021)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing](https://mlsanity.com/paper/2108.08807/) (2021)
- [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)
- [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)
- [SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements](https://mlsanity.com/paper/2104.07660/) (2021)
- [GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping](https://mlsanity.com/paper/1811.10983/) (2018)
- [Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction](https://mlsanity.com/paper/2012.01451/) (2020)
- [PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation](https://mlsanity.com/paper/2012.11310/) (2020)
- [DeepCloth: Neural Garment Representation for Shape and Style Editing](https://mlsanity.com/paper/2011.14619/) (2020)
- [DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation](https://mlsanity.com/paper/2009.02715/) (2020)
- [Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks](https://mlsanity.com/paper/2205.01355/) (2022)
- [Neural Cloth Simulation](https://mlsanity.com/paper/2212.11220/) (2022)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [Motion Guided Deep Dynamic 3D Garments](https://mlsanity.com/paper/2209.11449/) (2022)

## Abstract (arXiv)

Existing data-driven methods for garment animation, usually driven by linear skinning, although effective on tight garments, do not handle loose-fitting garments with complex deformations well. To address these limitations, we develop a garment generative model based on deformation decomposition to efficiently simulate loose garment deformation without directly using linear skinning. Specifically, we learn a garment generative space with the proposed generative model, where we decouple the latent representation into unposed deformed garments and dynamic offsets during the decoding stage. With explicit garment deformations decomposition, our generative model is able to generate complex pose-driven deformations on canonical garment shapes. Furthermore, we learn to transfer the body motions and previous state of the garment to the latent space to regenerate dynamic results. In addition, we introduce a detail enhancement module in an adversarial training setup to learn high-frequency wrinkles. We demonstrate our method outperforms state-of-the-art data-driven alternatives through extensive experiments and show qualitative and quantitative analysis of results.