欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
我们在场为综合的一个完全的框架由从繁体中文动画片学习的保存风格的 2D 动画片。与依靠重排或检索存在动画片序列的基于复用的途径相对照，我们试图与风格因式分解的想法产生因袭的动画片。明确地，以动画片字符的 2D 骨骼特征开始由一个改进 rotoscoping 系统提取了，我们在场获得风格基础和重量并且同时保存班的一个非否定的风格因式分解(NNSF ) 算法可分性。因此， factorized 风格基础能与异构的重量被相结合到重新综合保存风格的特征，然后这些特征在经由我们的建议驾驶 subkey 策略重塑过程的特性被用作开车来源。广泛的实验和例子表明建议框架的有效性。
英文摘要：
We present a complete framework for synthesizing style-preserving 2D cartoons by learning from traditional Chinese cartoons. In contrast to reusing-based approaches which rely on rearranging or retrieving existing cartoon sequences, we aim to generate stylized cartoons with the idea of style factorization. Specifically, starting with 2D skeleton features of cartoon characters extracted by an improved rotoscoping system, we present a non-negative style factorization （NNSF） algorithm to obtain style basis and weights and simultaneously preserve class separability. Thus, factorized style basis can be combined with heterogeneous weights to reynthesize style-preserving features, and then these features are used as the driving source in the character reshaping process via our proposed subkey-driving strategy. Extensive experiments and examples demonstrate the effectiveness of the proposed framework.
同期刊论文项目
同项目期刊论文
Real-time digitised shadow play performance method based on multi-point interactive controlling meth
期刊信息
位置：