Semantic SegmentationBased RealTime Shallow DepthofField Imaging Using a Single Smartphone Camera Recently, consumer smartphones have various camera functions to satisfy a variety of consumerxe2x80x99s needs. Shallow depthoffield imaging or shallow focusing technique can provide a photographic shallow focusing function. However, single camerabased economic implementation has limits in quality and accuracy. This paper presents a novel, efficient shallow focusing algorithm for consumer singlecamera smartphones. The proposed method consists of three functional steps: i) classification of foreground object and background using semantic segmentation, ii) blurring the background, and iii) composing the segmented object and blurred image. The proposed method can perform shallow focusing without detecting human faces, and produces better performance than existing singlecamera smartphones in the sense of subjective measure.

Synthetic Defocus and LookAhead Autofocus for Casual Videography. In cinema, large camera lenses create beautiful shallow depth of field (DOF), but make focusing difficult and expensive. Accurate cinema focus usually relies on a script and a person to control focus in realtime. Casual videographers often crave cinematic focus, but fail to achieve it. We either sacrifice shallow DOF, as in smartphone videos; or we struggle to deliver accurate focus, as in videos from larger cameras. This paper is about a new approach in the pursuit of cinematic focus for casual videography. We present a system that synthetically renders refocusable video from a deep DOF video shot with a smartphone, and analyzes future video frames to deliver contextaware autofocus for the current frame. To create refocusable video, we extend recent machine learning methods designed for still photography, contributing a new dataset for machine training, a rendering model better suited to cinema focus, and a filtering solution for temporal coherence. To choose focus accurately for each frame, we demonstrate autofocus that looks at upcoming video frames and applies AIassist modules such as motion, face, audio and saliency detection. We also show that autofocus benefits from machine learning and a largescale video dataset with focus annotation, where we use our RVRLAAF GUI to create this sizable dataset efficiently. We deliver, for example, a shallow DOF video where the autofocus transitions onto each person before she begins to speak. This is impossible for conventional camera autofocus because it would require seeing into the future.