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.

该研究提出了一个新的学习任务：在跨多个领域时，学习物体形状和外观的分离表示。通过利用每个领域的子集属性生成图像，实现中间分布的生成模型的学习。研究表明，通过利用不同iable直方图的视觉特征来表示物体外观，并通过优化生成器使得具有相同潜在外观因素但不同潜在形状因素的两个图像产生相似的直方图，可以跨领域进行准确和一致的外观和形状转换。

生成毛茸茸的汽车：在多个领域中解耦物体的形状和外表