Researchers at Meta AI Develop Multiface: A Dataset for Neural Face Rendering

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Modern Virtual Reality applications require technology that supports photo-realistic human face rendering and restoration. Due to the social nature of people and their ability to read and convey emotions from minor changes in facial expressions, minute artifacts can cause the uncanny valley, which can be detrimental to the user experience. In order to solve challenging issues like innovative view synthesis and view-dependent effects modeling, several contemporary 3D telepresence techniques now make use of deep learning models and neural rendering. These methods are typically data hungry, and the efficiency of those models is directly influenced by the architecture of the capturing device and data pipeline. In order to push the envelope in such photo-realistic human face models, a sizable dataset of high-quality, multi-view facial photos encompassing a wide range of expressions is necessary. Such a dataset was presented by Meta researchers in recent work.

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