If the video has long been mentioned as a widespread visualization form, the
animation sequence in the video is mentioned as storytelling for people.
Producing an animation requires intensive human labor from skilled professional
artists to obtain plausible animation in both content and motion direction,
incredibly for animations with complex content, multiple moving objects, and
dense movement. This paper presents an interactive framework to generate new
sequences according to the users' preference on the starting frame. The
critical contrast of our approach versus prior work and existing commercial
applications is that novel sequences with arbitrary starting frame are produced
by our system with a consistent degree in both content and motion direction. To
achieve this effectively, we first learn the feature correlation on the
frameset of the given video through a proposed network called RSFNet. Then, we
develop a novel path-finding algorithm, SDPF, which formulates the knowledge of
motion directions of the source video to estimate the smooth and plausible
sequences. The extensive experiments show that our framework can produce new
animations on the cartoon and natural scenes and advance prior works and
commercial applications to enable users to obtain more predictable results.

该研究论文提出了一种交互式框架，通过用户对起始帧的偏好生成新的序列，与之前的方法和现有商业应用相比，我们的方法在内容和运动方向上生成具有一致程度的任意起始帧的新序列。通过提出的网络 RSFNet 在给定视频的帧集上学习特征相关性，并开发了一种新的路径查找算法 SDPF，将源视频的运动方向知识建模，估计平滑可信的序列。广泛的实验表明，我们的框架能够在卡通和自然场景上生成新的动画，并推动以前的工作和商业应用以获得更可预测的结果。