Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers. More information and supplementary material can be found on our project webpage: https://motion-mode.github.io/

翻译：长曝光摄影能生成令人惊叹的图像，通过运动模糊呈现场景中的动态元素。它通常以两种模式使用：产生前景模糊或背景模糊效果。前景模糊图像传统上使用三脚架固定相机拍摄，呈现如柔滑水流或光轨等模糊的动态前景元素，背景景观则完全清晰。背景模糊图像（亦称追随摄影）在相机跟踪移动主体时拍摄，从而生成主体清晰、背景因相对运动而模糊的图像。这两种技术均极具挑战性，需要额外设备和高级技巧。本文描述了一种计算式连拍摄影系统，运行于手持智能手机相机应用，用户只需轻触快门按钮，即可全自动实现这些效果。我们的方法首先检测并分割出显著主体，然后通过多帧追踪场景运动并对齐图像，以保留所需的清晰度并生成美观的运动轨迹。我们捕获一组欠曝的连拍帧，并选择能够产生可控长度模糊轨迹的子集帧，无论场景或相机运动速度如何。我们预测帧间运动并合成运动模糊以填补输入帧之间的时间间隙。最后，将模糊图像与清晰的常规曝光合成，以保护人脸或场景中几乎不运动区域的清晰度，生成最终的高分辨率和高动态范围（HDR）照片。我们的系统使此前仅为专业人士保留的能力大众化，让大多数普通摄影师都能体验这一创意风格。更多信息和补充材料可在项目网页上找到：https://motion-mode.github.io/