5 citations · 7 across the 4 of their papers we have counts for
6 papers
Learnable Persistent Wrinkle Formation in Cloth Simulation
Deshan Gong, Ningtao Mao, Xiaoyuan Yang +3
The mechanical memory of fabrics often leads to persistent wrinkles, which reflect key physical properties and habitual wear patterns. Simulating these wrinkles accurately is essen…
Self-supervised Garment Dynamics with Persistent Wrinkles
Xiaoyuan Yang, Deshan Gong, Taku Komura +1
Self-supervised neural garment simulation has become popular due to its computational efficiency, good visual realism, and no reliance on training data. However, existing methods g…
Graphical X Splatting (GraphiXS): A Graphical Model for 4D Gaussian Splatting under Uncertainty
Doğa Yılmaz, Jialin Zhu, Deshan Gong +1
We propose a new framework to systematically incorporate data uncertainty in Gaussian Splatting. Being the new paradigm of neural rendering, Gaussian Splatting has been investigate…
Cloth Animation with Time-dependent Persistent Wrinkles
Deshan Gong, Yin Yang, Tianjia Shao +1
Persistent wrinkles are often observed on crumpled garments e.g., the wrinkles around the knees after sitting for a while. Such wrinkles can be easily recovered if not deformed for…
Bayesian Differentiable Physics for Cloth Digitalization
Deshan Gong, Ningtao Mao, He Wang
We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings, we propose to learn from data cap…
Fine-grained differentiable physics: a yarn-level model for fabrics
Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt +1
Differentiable physics modeling combines physics models with gradient-based learning to provide model explicability and data efficiency. It has been used to learn dynamics, solve i…