4,651 research outputs found
RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes
Radiance fields have gradually become a main representation of media.
Although its appearance editing has been studied, how to achieve
view-consistent recoloring in an efficient manner is still under explored. We
present RecolorNeRF, a novel user-friendly color editing approach for the
neural radiance fields. Our key idea is to decompose the scene into a set of
pure-colored layers, forming a palette. By this means, color manipulation can
be conducted by altering the color components of the palette directly. To
support efficient palette-based editing, the color of each layer needs to be as
representative as possible. In the end, the problem is formulated as an
optimization problem, where the layers and their blending weights are jointly
optimized with the NeRF itself. Extensive experiments show that our
jointly-optimized layer decomposition can be used against multiple backbones
and produce photo-realistic recolored novel-view renderings. We demonstrate
that RecolorNeRF outperforms baseline methods both quantitatively and
qualitatively for color editing even in complex real-world scenes.Comment: To appear in ACM Multimedia 2023. Project website is accessible at
https://sites.google.com/view/recolorner
Aesthetic Enhancement via Color Area and Location Awareness
Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without specifying their amount in an image. Also, it is still challenging to automatically assign individual palette colors to suitable image regions for maximizing image aesthetic quality. Motivated by these, we propose to construct a contribution-aware color palette from images with high aesthetic quality, enabling color transfer by matching the coloring and regional characteristics of an input image. We hence exploit public image datasets, extracting color composition and embedded color contribution features from aesthetic images to generate our proposed color palettes. We consider both image area ratio and image location as the color contribution features to extract. We have conducted quantitative experiments to demonstrate that our method outperforms existing methods through SSIM (Structural SIMilarity) and PSNR (Peak Signal to Noise Ratio) for objective image quality measurement and no-reference image assessment (NIMA) for image aesthetic scoring
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