Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels

TL;DR

This paper introduces a novel method for synthesizing multi-view bokeh and realistic image motion from a single portrait image by applying rotated dual-pixel blur kernels, enhancing the quality of synthetic depth and motion effects.

Contribution

It proposes a new blur synthesis technique using rotated kernels to generate multi-view images and realistic motion effects from a single input image.

Findings

High-quality multi-view bokeh synthesis demonstrated.

Realistic image motion with fewer artifacts achieved.

Qualitative validation shows improvement over existing methods.

Abstract

Portrait mode is widely available on smartphone cameras to provide an enhanced photographic experience. One of the primary effects applied to images captured in portrait mode is a synthetic shallow depth of field (DoF). The synthetic DoF (or bokeh effect) selectively blurs regions in the image to emulate the effect of using a large lens with a wide aperture. In addition, many applications now incorporate a new image motion attribute (NIMAT) to emulate background motion, where the motion is correlated with estimated depth at each pixel. In this work, we follow the trend of rendering the NIMAT effect by introducing a modification on the blur synthesis procedure in portrait mode. In particular, our modification enables a high-quality synthesis of multi-view bokeh from a single image by applying rotated blurring kernels. Given the synthesized multiple views, we can generate aesthetically realistic image motion similar to the NIMAT effect. We validate our approach qualitatively compared to the original NIMAT effect and other similar image motions, like Facebook 3D image. Our image motion demonstrates a smooth image view transition with fewer artifacts around the object boundary.