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Sloan Foundation fellowship","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000879","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2019,8,31]]},"abstract":"<jats:p>\n            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\n            <jats:italic>future<\/jats:italic>\n            video frames to deliver context-aware 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 AI-assist modules such as motion, face, audio and saliency detection. We also show that autofocus benefits from machine learning and a large-scale video dataset with focus annotation, where we use our RVR-LAAF GUI to create this sizable dataset efficiently. We deliver, for example, a shallow DOF video where the autofocus transitions onto each person\n            <jats:italic>before<\/jats:italic>\n            she begins to speak. This is impossible for conventional camera autofocus because it would require seeing into the future.\n          <\/jats:p>","DOI":"10.1145\/3306346.3323015","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T19:04:08Z","timestamp":1562958248000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Synthetic defocus and look-ahead autofocus for casual videography"],"prefix":"10.1145","volume":"38","author":[{"given":"Xuaner","family":"Zhang","sequence":"first","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Matzen","sequence":"additional","affiliation":[{"name":"Facebook Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivien","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dillon","family":"Yao","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chapman University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren","family":"Ng","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Youtube-8m: A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675","author":"Abu-El-Haija Sami","year":"2016"},{"key":"e_1_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Jonathan T Barron Andrew Adams YiChang Shih and Carlos Hern\u00e1ndez. 2015. 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