A wide range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation present in the data distribution. The extent to which a model represents such variations through its latent space can be judged by its ability to interpolate between images smoothly. However, most generative models mapping a fixed prior to the generated images lead to interpolation trajectories lacking smoothness and containing images of reduced quality. In this work, we propose a novel generative model that learns a flexible non-parametric prior over interpolation trajectories, conditioned on a pair of source and target images. Instead of relying on deterministic interpolation methods (such as linear or spherical interpolation in latent space), we devise a framework that learns a distribution of trajectories between two given images using Latent Second-Order Neural Ordinary Differential Equations. Through a hybrid combination of reconstruction and adversarial losses, the generator is trained to map the sampled points from these trajectories to sequences of realistic images that smoothly transition from the source to the target image. Through comprehensive qualitative and quantitative experiments, we demonstrate our approach's effectiveness in generating images of improved quality as well as its ability to learn a diverse distribution over smooth interpolation trajectories for any pair of real source and target images.
翻译:广泛的应用要求学习图像生成模型,其潜在空间有效捕捉到数据分布中存在差异的高层次因素。模型通过其潜在空间代表这种差异的程度,可以通过其在图像间顺利的内插能力来判断。然而,大多数基因化模型在生成图像之前绘制固定的图象,导致内插轨迹不光滑,并包含质量下降的图像。在这项工作中,我们提出了一个新型的基因化模型,通过这些轨迹和对抗性损失的混合组合,对从这些轨迹到从这些轨迹的抽样点进行绘图,以一对源和目标图像为条件。我们不依赖确定性的内插方法(如潜空线或球间插图),而是依靠其潜在空间的内插法,我们设计了一个框架,用以学习在两种特定图像之间分布的轨迹分布,即使用Latetent II- Order 普通差异的图象。我们通过从这些轨迹和对抗性损失的混合组合,对从这些轨迹到从源图像平稳地向目标图像转换的顺序进行测绘。我们通过全面的定性和定量分析,展示其真实质量和定量分析,我们从来源的图像的分布,以学习任何质量和定量分析方法,我们为制成各种质量和定量分析。