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Total Relighting:
Learning to Relight Portraits for Background Replacement
[Google Research]
* youtube
* project page
* pdf
We propose a novel system for portrait relighting and background replacement. Our technique includes alpha matting, relighting, and compositing. We demonstrate that each of these stages can be tackled in a sequential pipeline without the use of priors (e.g. background or illumination) and with no specialized acquisition techniques, using only a single RGB portrait image and a novel, target HDR lighting environment as inputs. We train our model using relit portraits of subjects captured in a light stage computational illumination system, which records multiple lighting conditions, high quality geometry, and accurate alpha mattes. To perform realistic relighting for compositing, we introduce a novel per-pixel lighting representation in a deep learning framework, which explicitly models the diffuse and the specular components of appearance.
#single_image #relighting #matting



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Total Relighting:
Learning to Relight Portraits for Background Replacement
[Google Research]
* youtube
* project page
* pdf
We propose a novel system for portrait relighting and background replacement. Our technique includes alpha matting, relighting, and compositing. We demonstrate that each of these stages can be tackled in a sequential pipeline without the use of priors (e.g. background or illumination) and with no specialized acquisition techniques, using only a single RGB portrait image and a novel, target HDR lighting environment as inputs. We train our model using relit portraits of subjects captured in a light stage computational illumination system, which records multiple lighting conditions, high quality geometry, and accurate alpha mattes. To perform realistic relighting for compositing, we introduce a novel per-pixel lighting representation in a deep learning framework, which explicitly models the diffuse and the specular components of appearance.
#single_image #relighting #matting

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The messaging service and social-media platform owes creditors roughly $700 million by the end of April, according to people briefed on the company’s plans and loan documents viewed by The Wall Street Journal. At the same time, Telegram Group Inc. must cover rising equipment and bandwidth expenses because of its rapid growth, despite going years without attempting to generate revenue.

A Telegram spokesman declined to comment on the bond issue or the amount of the debt the company has due. The spokesman said Telegram’s equipment and bandwidth costs are growing because it has consistently posted more than 40% year-to-year growth in users.

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