@inproceedings{
luo2024video,
title={Video Bokeh Rendering: Make Casual Videography Cinematic},
author={Yawen Luo and Min Shi and Liao Shen and Yachuan Huang and Zixuan Ye and Juewen Peng and Zhiguo Cao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=VcovhnCKSt}
}
Bokeh is a wide-aperture optical effect that creates aesthetic blurring in photography. However, achieving this effect typically demands expensive professional equipment and expertise. To make such cinematic techniques more accessible, bokeh rendering aims to generate the desired bokeh effects from all-in-focus inputs captured by smartphones. Previous efforts in bokeh rendering primarily focus on static images. However, when extended to video inputs, these methods exhibit flicker and artifacts due to a lack of temporal consistency modeling. Meanwhile, they cannot utilize information like occluded objects from adjacent frames, which are necessary for bokeh rendering. Moreover, the difficulties of capturing all-in-focus and bokeh video pairs result in a shortage of data for training video bokeh models. To tackle these challenges, we propose the Video Bokeh Renderer (VBR), the model designed specifically for video bokeh rendering. VBR leverages implicit feature space alignment and aggregation to model temporal consistency and exploit complementary information from adjacent frames. On the data front, we introduce the first Synthetic Video Bokeh (SVB) dataset, synthesizing authentic bokeh effects using ray-tracing techniques. Furthermore, to improve the robustness of the model to inaccurate disparity maps, we employ a set of augmentation strategies to simulate corrupted disparity inputs during training. Experimental results on both synthetic and real-world data demonstrate the effectiveness of our method. Code and dataset will be released.
Video Bokeh Rendering: Make Casual Videography Cinematic
Yawen Luo Min Shi Liao Shen Yachuan Huang Zixuan Ye Juewen Peng Zhiguo Cao