计算机科学
灵活性(工程)
一般化
参数化复杂度
服装
任务(项目管理)
限制
人工智能
机器学习
监督学习
蒙皮
人工神经网络
算法
工程类
机械工程
数学分析
统计
数学
考古
系统工程
历史
作者
Aoran Liu,Kun Hu,Wenxi Yue,Qiuxia Wu,Zhiyong Wang
标识
DOI:10.1109/icme55011.2023.00114
摘要
Dynamic 3D garment simulation has various applications in many domains. Recently, self-supervised learning for this task has been studied to reduce annotation costs. However, different material characteristics of garments have been rarely explored, limiting the generalization and flexibility of existing methods. Therefore, in this paper, a novel self-supervised deep learning architecture is proposed, namely Material-aware Self-supervised Network (MSN), as a material-aware approach for dynamically simulating garments with different materials. Specifically, a material-aware parameterized regressor is introduced based on the observation that material characteristics change continuously regarding the fabric parameters. As a result, MSN realises real-time garment simulation with various material properties without model re-training. Moreover, to simulate garments of different categories (e.g., t-shirts vs. dresses), a sampling-based linear skinning strategy is studied in MSN. Comprehensive experiments on the widely used AMASS dataset demonstrated the effectiveness of MSN both quantitatively and qualitatively.
科研通智能强力驱动
Strongly Powered by AbleSci AI