Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we present an automatic method that allows generating human cinemagraphs from single RGB images. We investigate the problem in the context of dressed humans under the wind. At the core of our method is a novel cyclic neural network that produces looping cinemagraphs for the target loop duration. To circumvent the problem of collecting real data, we demonstrate that it is possible, by working in the image normal space, to learn garment motion dynamics on synthetic data and generalize to real data. We evaluate our method on both synthetic and real data and demonstrate that it is possible to create compelling and plausible cinemagraphs from single RGB images.

翻译：动态影像（Cinemagraphs）是通过在静态图像中添加微妙运动生成的短循环视频，这类媒介广受欢迎且极具吸引力。然而，动态影像的自动生成仍属研究不足的领域，现有方案需艺术家进行繁琐的低层级手工创作。本文提出一种自动方法，可从单张RGB图像生成人物动态影像。我们在风吹着装人体的场景中探究该问题，其核心是新颖的循环神经网络（CycleNet），能够为目标循环时长生成连续动态影像。为规避真实数据采集难题，我们证明：通过采用图像法向空间处理，可在合成数据中学习服装运动动力学特征，并泛化至真实数据。我们在合成数据集与真实数据集上评估了该方法，证实了从单张RGB图像生成真实可信动态影像的可行性。