论文标题
绘画许多过去：综合绘画的延时视频
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings
论文作者
论文摘要
我们介绍了一项新的视频综合任务：综合时间流逝视频，描绘了给定绘画的创建方式。艺术家使用刷子，笔画和颜色的独特组合绘画。通常有许多可能的方法来创建给定的绘画。我们的目标是学会捕捉这种丰富的可能性。 创建长期视频的分布是基于学习的视频综合方法的挑战。我们提出了一个概率模型，鉴于完整的绘画的单个图像，它偶然综合了绘画过程的步骤。我们将此模型作为卷积神经网络实施，并引入了一种新颖的培训计划，以使绘画时间段的数据集学习。我们证明该模型可用于采样许多时间步骤，从而实现长期随机视频综合。我们评估了从视频网站收集的数字和水彩画的方法，并表明人类评估者发现我们的合成视频类似于真实艺术家制作的时间流逝视频。我们的代码可在https://xamyzhao.github.io/timecraft上找到。
We introduce a new video synthesis task: synthesizing time lapse videos depicting how a given painting might have been created. Artists paint using unique combinations of brushes, strokes, and colors. There are often many possible ways to create a given painting. Our goal is to learn to capture this rich range of possibilities. Creating distributions of long-term videos is a challenge for learning-based video synthesis methods. We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process. We implement this model as a convolutional neural network, and introduce a novel training scheme to enable learning from a limited dataset of painting time lapses. We demonstrate that this model can be used to sample many time steps, enabling long-term stochastic video synthesis. We evaluate our method on digital and watercolor paintings collected from video websites, and show that human raters find our synthetic videos to be similar to time lapse videos produced by real artists. Our code is available at https://xamyzhao.github.io/timecraft.
