# Motion Guided Deep Dynamic 3D Garments

arXiv: https://arxiv.org/abs/2209.11449 · HTML: https://mlsanity.com/paper/2209.11449/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 23 Sep 2022.

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

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Learning Mesh-Based Simulation with Graph Networks](https://mlsanity.com/paper/2010.03409/) (2020)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (2021)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [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)
- [LASR: Learning Articulated Shape Reconstruction from a Monocular Video](https://mlsanity.com/paper/2105.02976/) (2021)
- [Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing](https://mlsanity.com/paper/2108.08807/) (2021)
- [DeepWrinkles: Accurate and Realistic Clothing Modeling](https://mlsanity.com/paper/1808.03417/) (2018)
- [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)
- [PBNS: Physically Based Neural Simulator for Unsupervised Garment Pose Space Deformation](https://mlsanity.com/paper/2012.11310/) (2020)
- [Learning a Shared Shape Space for Multimodal Garment Design](https://mlsanity.com/paper/1806.11335/) (2018)
- [DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation](https://mlsanity.com/paper/2009.02715/) (2020)
- [SimulCap : Single-View Human Performance Capture with Cloth Simulation](https://mlsanity.com/paper/1903.06323/) (2019)
- [Codimensional Incremental Potential Contact](https://mlsanity.com/paper/2012.04457/) (2020)
- [A Pixel-Based Framework for Data-Driven Clothing](https://mlsanity.com/paper/1812.01677/) (2018)
- [P-Cloth: Interactive Complex Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integrators](https://mlsanity.com/paper/2008.00409/) (2020)
- [Deep Detail Enhancement for Any Garment](https://mlsanity.com/paper/2008.04367/) (2020)

## Built on by (most cited first, 19 of 19)

- [HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics](https://mlsanity.com/paper/2212.07242/) (2022)
- [ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns](https://mlsanity.com/paper/2305.14100/) (2023)
- [Towards Multi-Layered 3D Garments Animation](https://mlsanity.com/paper/2305.10418/) (2023)
- [ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians](https://mlsanity.com/paper/2406.16815/) (2024)
- [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)
- [PhysGraph: Physics-Based Integration Using Graph Neural Networks](https://mlsanity.com/paper/2301.11841/) (2023)
- [Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation](https://mlsanity.com/paper/2407.11266/) (2024)
- [SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging Garments](https://mlsanity.com/paper/2308.02827/) (2023)
- [DiffusedWrinkles: A Diffusion-Based Model for Data-Driven Garment Animation](https://mlsanity.com/paper/2503.18370/) (2025)
- [D-Garment: Physically Grounded Latent Diffusion for Dynamic Garment Deformations](https://mlsanity.com/paper/2504.03468/) (2025)
- [Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting](https://mlsanity.com/paper/2401.03108/) (2024)
- [A Comparative Study of Garment Draping Techniques](https://mlsanity.com/paper/2405.11056/) (2024)
- [Neural Garment Dynamic Super-Resolution](https://mlsanity.com/paper/2412.06285/) (2024)
- [Learning 3D Garment Animation from Trajectories of A Piece of Cloth](https://mlsanity.com/paper/2501.01393/) (2025)
- [Towards Loose-Fitting Garment Animation via Generative Model of Deformation Decomposition](https://mlsanity.com/paper/2312.14619/) (2023)
- [Learning High-Fidelity Cloth Animation via Skinning-Free Image Transfer](https://mlsanity.com/paper/2512.05593/) (2025)
- [UNIC: Neural Garment Deformation Field for Real-time Clothed Character Animation](https://mlsanity.com/paper/2603.25580/) (2026)
- [DiT-Garment: Garment Dynamics with Diffusion Transformers](https://mlsanity.com/paper/2609.18510/) (2026)

## Abstract (arXiv)

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative rollout prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives.