DEEAPR: Controllable Depth Enhancement via Adaptive Parametric Feature Rotation

Hanul Shin, Youngchan Song, Soo Min Kang

Abstract: Understanding depth of an image provides viewers with a better interpretation of the 3D structures within an image. Photographers utilize numerous factors that can affect depth perception to aesthetically improve a scene. Unfortunately, controlling depth perception after the image has been captured is a difficult process as it requires accurate and explicit depth information. Also, defining a quantitative metric of a subjective quality (i.e., depth perception) is difficult which makes supervised learning a great challenge. To this end, we propose DEpth Enhancement via Adaptive Parametric feature Rotation (DEEAPR), which modulates the perceptual depth of an input scene using a single control parameter without the need for explicit depth information. We first embed content-independent depth perception of a scene by visual representation learning. Then, we train the controllable depth enhancer network with a novel modulator, parametric feature rotation block (PFRB), that allows for continuous modulation of a representative feature. We demonstrate the effectiveness of our proposed approach by verifying each component through an ablation study and comparison to other controllable methods.

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