生成毛茸茸的汽车：在多个领域解开对象形状和外观
Generating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains
摘要：
我们考虑了跨多个域学习对象形状和外观的解纠缠表示的新任务（例如，狗和汽车）。我们的目标是学习一个生成模型，该模型学习一个中间分布，该中间分布从每个域借用一个属性子集，从而能够生成不专门存在于任何域中的图像。这个具有挑战性的问题需要从每个域中准确地解开对象形状、外观和背景，以便可以互换来自两个域的外观和形状因子。我们增强了现有的方法，可以在一个单一的领域内解开因素，但努力做到跨域。我们的关键技术贡献是用视觉特征的可区分直方图来表示对象外观，并优化生成器，使得具有相同潜在外观因子但不同潜在形状因子的两个图像产生相似的直方图。在多个多域数据集上，我们证明了我们的方法可以实现跨域的准确和一致的外观和形状转移。
英文摘要：
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that learns an intermediate distribution, which borrows a subset of properties from each domain, enabling the generation of images that did not exist in any domain exclusively. This challenging problem requires an accurate disentanglement of object shape, appearance, and background from each domain, so that the appearance and shape factors from the two domains can be interchanged. We augment an existing approach that can disentangle factors within a single domain but struggles to do so across domains. Our key technical contribution is to represent object appearance with a differentiable histogram of visual features, and to optimize the generator so that two images with the same latent appearance factor but different latent shape factors produce similar histograms. On multiple multi-domain datasets, we demonstrate our method leads to accurate and consistent appearance and shape transfer across domains.
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。
