To alleviate human labor in redrawing keyframes with ordered vector strokes for automatic inbetweening, we for the first time propose a joint stroke tracing and correspondence approach. Given consecutive raster keyframes along with a single vector image of the starting frame as a guidance, the approach generates vector drawings for the remaining keyframes while ensuring one-to-one stroke correspondence. Our framework trained on clean line drawings generalizes to rough sketches, and the generated results can be imported into inbetweening systems to produce inbetween sequences. Hence, the method is compatible with standard 2D animation workflow. An adaptive spatial transformation module (ASTM) is introduced to handle non-rigid motions and stroke distortion. We collect a dataset for training with 10k+ pairs of raster frames and their vector drawings with stroke correspondence. Comprehensive validations on real clean and rough animated frames manifest the effectiveness of our method and superiority to existing methods.

为了减轻使用有序矢量笔划重绘关键帧以进行自动中间插入的人工劳动，我们首次提出了联合笔划跟踪和对应方法。给定连续的光栅关键帧以及起始帧的单个矢量图像作为指导，该方法为剩余关键帧生成矢量图形，同时确保一对一的笔画对应。我们在干净的线条图上训练的框架可以推广到粗略的草图，并且生成的结果可以导入到中间系统中以生成中间序列。因此，该方法与标准 2D 动画工作流程兼容。引入自适应空间变换模块（ASTM）来处理非刚性运动和笔划变形。我们收集了一个数据集用于训练，其中包含超过 10k 对光栅帧及其带有笔划对应的矢量图形。对真实干净和粗糙动画帧的全面验证表明了我们方法的有效性以及相对于现有方法的优越性。