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.

翻译：若视频长期被视为一种广泛应用的视觉表现形式，则其中的动画序列可理解为面向观众的叙事载体。制作动画需要专业艺术家投入大量人力劳动，才能获得内容和运动方向均合理的动画效果，尤其对于包含复杂内容、多个运动对象及密集运动的动画场景。本文提出一种交互式框架，可根据用户对起始帧的偏好生成新的序列。与现有研究及商业应用的关键区别在于，我们的系统能够生成任意起始帧的新序列，并在内容和运动方向上保持一致性。为高效实现这一目标，我们首先通过提出的RSFNet网络学习给定视频帧集上的特征关联，进而开发了一种新颖的路径寻优算法SDPF，该算法通过建模源视频的运动方向知识来估计平滑且合理的序列。大量实验表明，我们的框架能够在卡通及自然场景中生成新动画，并推动了现有研究与商业应用的发展，使用户能够获得更具可预测性的结果。