Novel view synthesis from a single input image is a challenging task, where the goal is to generate a new view of a scene from a desired camera pose that may be separated by a large motion. The highly uncertain nature of this synthesis task due to unobserved elements within the scene (i.e., occlusion) and outside the field-of-view makes the use of generative models appealing to capture the variety of possible outputs. In this paper, we propose a novel generative model which is capable of producing a sequence of photorealistic images consistent with a specified camera trajectory, and a single starting image. Our approach is centred on an autoregressive conditional diffusion-based model capable of interpolating visible scene elements, and extrapolating unobserved regions in a view, in a geometrically consistent manner. Conditioning is limited to an image capturing a single camera view and the (relative) pose of the new camera view. To measure the consistency over a sequence of generated views, we introduce a new metric, the thresholded symmetric epipolar distance (TSED), to measure the number of consistent frame pairs in a sequence. While previous methods have been shown to produce high quality images and consistent semantics across pairs of views, we show empirically with our metric that they are often inconsistent with the desired camera poses. In contrast, we demonstrate that our method produces both photorealistic and view-consistent imagery.

翻译：从单一输入图像进行新视角合成是一项具有挑战性的任务，其目标是从期望的相机姿态（可能伴随大范围运动）生成场景的新视角。由于场景中未观测元素（即遮挡）及视场外区域的高度不确定性，生成模型在捕捉多样化输出方面具有显著优势。本文提出一种新型生成模型，能够生成与指定相机轨迹及单张起始图像一致的光真实感图像序列。该方法的核心是基于自回归条件扩散的模型，能够以几何一致的方式插值可见场景元素并外推视图中未观测区域。条件输入仅限于单视图图像和新相机视角的（相对）姿态。为衡量生成视图序列的一致性，我们引入新指标——阈值化对称对极距离（TSED），用于量化序列中一致性帧对的数量。现有方法虽能生成高质量图像并保持跨视图语义一致性，但经验性实验表明，其图像往往与期望相机姿态不一致。相比之下，本方法既生成光真实感图像，又确保视图一致性。