论文标题
通过深入生成学习的综合逼真的图像
Synthesizing Photorealistic Images with Deep Generative Learning
论文作者
论文摘要
本文的目的是为解决各种视觉合成和发电任务提供我的研究贡献，包括图像翻译，图像完成和完整的场景分解。该论文由五项作品组成，每个作品都提出了一种新的基于学习的方法，用于合成具有合理内容的图像以及视觉上现实的外观。每项工作都证明了所提出的方法对图像综合的优越性，并进一步促进了其他任务，例如深度估计。
The goal of this thesis is to present my research contributions towards solving various visual synthesis and generation tasks, comprising image translation, image completion, and completed scene decomposition. This thesis consists of five pieces of work, each of which presents a new learning-based approach for synthesizing images with plausible content as well as visually realistic appearance. Each work demonstrates the superiority of the proposed approach on image synthesis, with some further contributing to other tasks, such as depth estimation.
