We propose a method for real-time cloth deformation using neural networks, especially for draping a garment on a human body. The computational overhead of most of the existing learning methods for cloth deformation often limits their use in interactive applications. Employing a two-stage training process, our method predicts garment deformations in real-time. In the first stage, a graph neural network extracts cloth vertex features which are compressed into a latent vector with a mesh convolution network. We then decode the latent vector to blend shape weights, which are fed to a trainable blend shape module. In the second stage, we freeze the latent extraction and train a latent predictor network. The predictor uses a subset of the inputs from the first stage, ensuring that inputs are restricted to those which are readily available in a typical game engine. Then, during inference, the latent predictor predicts the compacted latent which is processed by the decoder and blend shape networks from the first stage. Our experiments demonstrate that our method effectively balances computational efficiency and realistic cloth deformation, making it suitable for real-time use in applications such as games.
@mastersthesis{library994526,title={Real-Time Neural Cloth Deformation using a Compact Latent Space and a Latent Vector Predictor},year={2024},month=sep,school={Concordia University},note={Master's thesis. The accepted paper version for ECCV workshop will be updated soon},author={Lee, Chanhaeng},url={https://spectrum.library.concordia.ca/id/eprint/994526/},}
