论文标题
智能画家：带重采样扩散模型的图片组成
Intelligent Painter: Picture Composition With Resampling Diffusion Model
论文作者
论文摘要
您是否曾经以为自己可以成为一名聪明的画家？这意味着您可以将图片绘制出一些预期的对象或理想场景的图片。这与无法确定特定对象的位置的正常介入方法不同。在本文中，我们提出了一位聪明的画家，该画家一口气就会产生一个人的虚构场景，并有明确的提示。我们提出了一种重新采样策略，以根据特定位置的输入主题来智能地构成无条件统一的图片，以降级扩散概率模型（DDPM）。通过利用扩散属性，我们有效地重新采样以产生逼真的图片。实验结果表明，我们的重新采样方法有效地有效地产生了产生的输出的语义含义，并产生了较少的模糊输出。对图像质量评估的定量分析表明，与最先进的方法相比，我们的方法会产生更高的感知质量图像。
Have you ever thought that you can be an intelligent painter? This means that you can paint a picture with a few expected objects in mind, or with a desirable scene. This is different from normal inpainting approaches for which the location of specific objects cannot be determined. In this paper, we present an intelligent painter that generate a person's imaginary scene in one go, given explicit hints. We propose a resampling strategy for Denoising Diffusion Probabilistic Model (DDPM) to intelligently compose unconditional harmonized pictures according to the input subjects at specific locations. By exploiting the diffusion property, we resample efficiently to produce realistic pictures. Experimental results show that our resampling method favors the semantic meaning of the generated output efficiently and generates less blurry output. Quantitative analysis of image quality assessment shows that our method produces higher perceptual quality images compared with the state-of-the-art methods.
