# Learning Anchor Transformations for 3D Garment Animation

arXiv: https://arxiv.org/abs/2304.00761 · HTML: https://mlsanity.com/paper/2304.00761/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [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)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (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)
- [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)
- [GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping](https://mlsanity.com/paper/1811.10983/) (2018)
- [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)
- [3DPeople: Modeling the Geometry of Dressed Humans](https://mlsanity.com/paper/1904.04571/) (2019)
- [DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation](https://mlsanity.com/paper/2009.02715/) (2020)
- [GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion](https://mlsanity.com/paper/2104.05177/) (2021)
- [Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks](https://mlsanity.com/paper/2205.01355/) (2022)
- [Point-Based Modeling of Human Clothing](https://mlsanity.com/paper/2104.08230/) (2021)
- [Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction](https://mlsanity.com/paper/2108.08478/) (2021)
- [Neural Point-based Shape Modeling of Humans in Challenging Clothing](https://mlsanity.com/paper/2209.06814/) (2022)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [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)

## Built on by (most cited first, 7 of 7)

- [FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation](https://mlsanity.com/paper/2412.02690/) (2024)
- [Design2Cloth: 3D Cloth Generation from 2D Masks](https://mlsanity.com/paper/2404.02686/) (2024)
- [Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation](https://mlsanity.com/paper/2407.11266/) (2024)
- [A Neural-Network-Based Approach for Loose-Fitting Clothing](https://mlsanity.com/paper/2404.16896/) (2024)
- [SpringTime: Learning Simulatable Models of Cloth with Spatially-varying Constitutive Properties](https://mlsanity.com/paper/2507.21288/) (2025)
- [NGL: Natural Garment Language for Training-Free Sewing Pattern Estimation](https://mlsanity.com/paper/2602.20700/) (2026)
- [Learning High-Fidelity Cloth Animation via Skinning-Free Image Transfer](https://mlsanity.com/paper/2512.05593/) (2025)

## Abstract (arXiv)

This paper proposes an anchor-based deformation model, namely AnchorDEF, to predict 3D garment animation from a body motion sequence. It deforms a garment mesh template by a mixture of rigid transformations with extra nonlinear displacements. A set of anchors around the mesh surface is introduced to guide the learning of rigid transformation matrices. Once the anchor transformations are found, per-vertex nonlinear displacements of the garment template can be regressed in a canonical space, which reduces the complexity of deformation space learning. By explicitly constraining the transformed anchors to satisfy the consistencies of position, normal and direction, the physical meaning of learned anchor transformations in space is guaranteed for better generalization. Furthermore, an adaptive anchor updating is proposed to optimize the anchor position by being aware of local mesh topology for learning representative anchor transformations. Qualitative and quantitative experiments on different types of garments demonstrate that AnchorDEF achieves the state-of-the-art performance on 3D garment deformation prediction in motion, especially for loose-fitting garments.