# Multi-feature super-resolution network for cloth wrinkle synthesis

arXiv: https://arxiv.org/abs/2004.04351 · HTML: https://mlsanity.com/paper/2004.04351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks](https://mlsanity.com/paper/2112.06397/) (2021)
- [Deep Deformation Detail Synthesis for Thin Shell Models](https://mlsanity.com/paper/2102.11541/) (2021)
- [Neural Garment Dynamic Super-Resolution](https://mlsanity.com/paper/2412.06285/) (2024)

## Abstract (arXiv)

Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling behaviors. Data-driven methods provide an alternative solution. It typically synthesizes cloth animation at a much lower computational cost, and also creates wrinkling effects that highly resemble the much controllable training data. In this paper we propose a deep learning based method for synthesizing cloth animation with high resolution meshes. To do this we first create a dataset for training: a pair of low and high resolution meshes are simulated and their motions are synchronized. As a result the two meshes exhibit similar large-scale deformation but different small wrinkles. Each simulated mesh pair are then converted into a pair of low and high resolution "images" (a 2D array of samples), with each sample can be interpreted as any of three features: the displacement, the normal and the velocity. With these image pairs, we design a multi-feature super-resolution (MFSR) network that jointly train an upsampling synthesizer for the three features. The MFSR architecture consists of two key components: a sharing module that takes multiple features as input to learn low-level representations from corresponding super-resolution tasks simultaneously; and task-specific modules focusing on various high-level semantics. Frame-to-frame consistency is well maintained thanks to the proposed kinematics-based loss function. Our method achieves realistic results at high frame rates: 12-14 times faster than traditional physical simulation. We demonstrate the performance of our method with various experimental scenes, including a dressed character with sophisticated collisions.