A Pixel-Based Framework for Data-Driven Clothing
arXiv:1812.01677
Abstract
With the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation as an RGB image in a two dimensional pattern space. Then a three dimensional animation of cloth is equivalent to a sequence of two dimensional RGB images, which in turn are driven/choreographed via animation parameters such as joint angles. This allows us to leverage popular CNNs to learn cloth deformations in image space. The two dimensional cloth pixels are extended into the real world via standard body skinning techniques, after which the RGB values are interpreted as texture offsets and displacement maps. Notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. Additionally, we discuss how standard image based techniques such as image partitioning for higher resolution, GANs for merging partitioned image regions back together, etc., can readily be incorporated into our framework.
References in corpus (10)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Deep Residual Learning for Image Recognition
- Geometric deep learning: going beyond Euclidean data
- Deep Convolutional Networks on Graph-Structured Data
- Fully Convolutional Networks for Semantic Segmentation
- Generative Image Inpainting with Contextual Attention
- Generative Face Completion
- Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
- Variational Autoencoders for Deforming 3D Mesh Models
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- Deep Detail Enhancement for Any Garment
- Alternating ConvLSTM: Learning Force Propagation with Alternate State Updates
- Dynamic Neural Garments
- The Power of Points for Modeling Humans in Clothing