3D View Optimization for Improving Image Aesthetics

TL;DR

This paper presents a novel 3D view optimization method that reconstructs scenes from images to improve aesthetic quality, outperforming traditional 2D editing techniques in both qualitative and quantitative evaluations.

Contribution

It introduces a pioneering 3D-based approach for aesthetic enhancement by reconstructing scenes and optimizing camera parameters, expanding beyond limited 2D manipulation methods.

Findings

Our method achieves higher aesthetic scores than 2D editing techniques.

Qualitative assessments show more visually pleasing results.

Quantitative metrics confirm improved aesthetic quality.

Abstract

Achieving aesthetically pleasing photography necessitates attention to multiple factors, including composition and capture conditions, which pose challenges to novices. Prior research has explored the enhancement of photo aesthetics post-capture through 2D manipulation techniques; however, these approaches offer limited search space for aesthetics. We introduce a pioneering method that employs 3D operations to simulate the conditions at the moment of capture retrospectively. Our approach extrapolates the input image and then reconstructs the 3D scene from the extrapolated image, followed by an optimization to identify camera parameters and image aspect ratios that yield the best 3D view with enhanced aesthetics. Comparative qualitative and quantitative assessments reveal that our method surpasses traditional 2D editing techniques with superior aesthetics.