逆向绘画：重建绘画过程
Inverse Painting: Reconstructing The Painting Process
AI总结：
本文提出一种自回归图像生成方法，结合文本与区域理解及基于扩散的渲染器，从输入画作逆向重建绘画过程的时间推移视频，并能泛化至多种艺术风格。
摘要：
给定一幅输入画作，我们重建一段展示其可能被绘制过程的时间推移视频。我们将其表述为一个自回归图像生成问题，其中初始空白的“画布”被迭代更新。该模型通过在大量绘画视频上训练来向真实艺术家学习。我们的方法结合了文本和区域理解来定义一组绘画“指令”，并使用一种新颖的基于扩散的渲染器更新画布。该方法外推超越了其训练所用的有限的丙烯风格画作，对广泛的艺术风格和流派展现出合理的结果。
英文摘要：
Given an input painting, we reconstruct a time-lapse video of how it may have been painted. We formulate this as an autoregressive image generation problem, in which an initially blank "canvas" is iteratively updated. The model learns from real artists by training on many painting videos. Our approach incorporates text and region understanding to define a set of painting "instructions" and updates the canvas with a novel diffusion-based renderer. The method extrapolates beyond the limited, acrylic style paintings on which it has been trained, showing plausible results for a wide range of artistic styles and genres.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
