# Dynamic Neural Garments

arXiv: https://arxiv.org/abs/2102.11811 · HTML: https://mlsanity.com/paper/2102.11811/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 97 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 30 of 49)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [Video-to-Video Synthesis](https://mlsanity.com/paper/1808.06601/) (2018)
- [Neural Point-Based Graphics](https://mlsanity.com/paper/1906.08240/) (2019)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [ARCH: Animatable Reconstruction of Clothed Humans](https://mlsanity.com/paper/2004.04572/) (2020)
- [Deferred Neural Rendering: Image Synthesis using Neural Textures](https://mlsanity.com/paper/1904.12356/) (2019)
- [SEAN: Image Synthesis with Semantic Region-Adaptive Normalization](https://mlsanity.com/paper/1911.12861/) (2019)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [Animating Arbitrary Objects via Deep Motion Transfer](https://mlsanity.com/paper/1812.08861/) (2018)
- [Neural Volumes: Learning Dynamic Renderable Volumes from Images](https://mlsanity.com/paper/1906.07751/) (2019)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis](https://mlsanity.com/paper/1909.12224/) (2019)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [Visual Attribute Transfer through Deep Image Analogy](https://mlsanity.com/paper/1705.01088/) (2017)
- [DwNet: Dense warp-based network for pose-guided human video generation](https://mlsanity.com/paper/1910.09139/) (2019)
- [Contact and Human Dynamics from Monocular Video](https://mlsanity.com/paper/2007.11678/) (2020)
- [Unsupervised Learning of Object Structure and Dynamics from Videos](https://mlsanity.com/paper/1906.07889/) (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)

## Built on by (most cited first, 27 of 27)

- [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)
- [SPAMs: Structured Implicit Parametric Models](https://mlsanity.com/paper/2201.08141/) (2022)
- [Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing](https://mlsanity.com/paper/2206.15470/) (2022)
- [Data-Free Learning of Reduced-Order Kinematics](https://mlsanity.com/paper/2305.03846/) (2023)
- [Structure-Preserving 3D Garment Modeling with Neural Sewing Machines](https://mlsanity.com/paper/2211.06701/) (2022)
- [Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey](https://mlsanity.com/paper/2406.19675/) (2024)
- [Dance In the Wild: Monocular Human Animation with Neural Dynamic Appearance Synthesis](https://mlsanity.com/paper/2111.05916/) (2021)
- [N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks](https://mlsanity.com/paper/2112.06397/) (2021)
- [Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera](https://mlsanity.com/paper/2203.12780/) (2022)
- [Learning rigid-body simulators over implicit shapes for large-scale scenes and vision](https://mlsanity.com/paper/2405.14045/) (2024)
- [GAPS: Geometry-Aware, Physics-Based, Self-Supervised Neural Garment Draping](https://mlsanity.com/paper/2312.01490/) (2023)
- [MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video](https://mlsanity.com/paper/2303.10275/) (2023)
- [Learning Anchor Transformations for 3D Garment Animation](https://mlsanity.com/paper/2304.00761/) (2023)
- [PhysGraph: Physics-Based Integration Using Graph Neural Networks](https://mlsanity.com/paper/2301.11841/) (2023)
- [PhysGaia: A Physics-Aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis](https://mlsanity.com/paper/2506.02794/) (2025)
- [SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging Garments](https://mlsanity.com/paper/2308.02827/) (2023)
- [AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model](https://mlsanity.com/paper/2401.15348/) (2024)
- [DiffusedWrinkles: A Diffusion-Based Model for Data-Driven Garment Animation](https://mlsanity.com/paper/2503.18370/) (2025)
- [Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks](https://mlsanity.com/paper/2201.10122/) (2022)
- [A Neural-Network-Based Approach for Loose-Fitting Clothing](https://mlsanity.com/paper/2404.16896/) (2024)
- [Human Geometry Distribution for 3D Animation Generation](https://mlsanity.com/paper/2512.07459/) (2025)
- [Neural Garment Dynamic Super-Resolution](https://mlsanity.com/paper/2412.06285/) (2024)
- [Towards Loose-Fitting Garment Animation via Generative Model of Deformation Decomposition](https://mlsanity.com/paper/2312.14619/) (2023)
- [Garment Animation NeRF with Color Editing](https://mlsanity.com/paper/2407.19774/) (2024)
- [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)

## Abstract (arXiv)

A vital task of the wider digital human effort is the creation of realistic garments on digital avatars, both in the form of characteristic fold patterns and wrinkles in static frames as well as richness of garment dynamics under avatars' motion. Existing workflow of modeling, simulation, and rendering closely replicates the physics behind real garments, but is tedious and requires repeating most of the workflow under changes to characters' motion, camera angle, or garment resizing. Although data-driven solutions exist, they either focus on static scenarios or only handle dynamics of tight garments. We present a solution that, at test time, takes in body joint motion to directly produce realistic dynamic garment image sequences. Specifically, given the target joint motion sequence of an avatar, we propose dynamic neural garments to jointly simulate and render plausible dynamic garment appearance from an unseen viewpoint. Technically, our solution generates a coarse garment proxy sequence, learns deep dynamic features attached to this template, and neurally renders the features to produce appearance changes such as folds, wrinkles, and silhouettes. We demonstrate generalization behavior to both unseen motion and unseen camera views. Further, our network can be fine-tuned to adopt to new body shape and/or background images. We also provide comparisons against existing neural rendering and image sequence translation approaches, and report clear quantitative improvements.