论文标题
学会在图像中操纵单个对象
Learning to Manipulate Individual Objects in an Image
论文作者
论文摘要
我们描述了一种训练具有（大约）独立和本地化的潜在因素的生成模型的方法。这意味着扰动潜在变量仅影响与对象相对应的综合图像的局部区域。与其他无监督的生成模型不同，我们的启用以对象为中心的操作，而无需对象级注释或任何形式的注释。我们方法的关键是通过上下文信息分离损失和感知周期一致性强制实施的空间分解的组合，这是由于损失而造成的，该损失会损害图像分区的变化，以应对潜在因素的扰动。我们测试了我们的方法可以在现有数据集中独立控制可变性的空间和语义因素的能力，并引入了两个新的，以突出当前方法的局限性。
We describe a method to train a generative model with latent factors that are (approximately) independent and localized. This means that perturbing the latent variables affects only local regions of the synthesized image, corresponding to objects. Unlike other unsupervised generative models, ours enables object-centric manipulation, without requiring object-level annotations, or any form of annotation for that matter. The key to our method is the combination of spatial disentanglement, enforced by a Contextual Information Separation loss, and perceptual cycle-consistency, enforced by a loss that penalizes changes in the image partition in response to perturbations of the latent factors. We test our method's ability to allow independent control of spatial and semantic factors of variability on existing datasets and also introduce two new ones that highlight the limitations of current methods.
