1,841 research outputs found
One-shot Detail Retouching with Patch Space Neural Field based Transformation Blending
Photo retouching is a difficult task for novice users as it requires expert
knowledge and advanced tools. Photographers often spend a great deal of time
generating high-quality retouched photos with intricate details. In this paper,
we introduce a one-shot learning based technique to automatically retouch
details of an input image based on just a single pair of before and after
example images. Our approach provides accurate and generalizable detail edit
transfer to new images. We achieve these by proposing a new representation for
image to image maps. Specifically, we propose neural field based transformation
blending in the patch space for defining patch to patch transformations for
each frequency band. This parametrization of the map with anchor
transformations and associated weights, and spatio-spectral localized patches,
allows us to capture details well while staying generalizable. We evaluate our
technique both on known ground truth filtes and artist retouching edits. Our
method accurately transfers complex detail retouching edits
On the Importance of Visual Context for Data Augmentation in Scene Understanding
Performing data augmentation for learning deep neural networks is known to be
important for training visual recognition systems. By artificially increasing
the number of training examples, it helps reducing overfitting and improves
generalization. While simple image transformations can already improve
predictive performance in most vision tasks, larger gains can be obtained by
leveraging task-specific prior knowledge. In this work, we consider object
detection, semantic and instance segmentation and augment the training images
by blending objects in existing scenes, using instance segmentation
annotations. We observe that randomly pasting objects on images hurts the
performance, unless the object is placed in the right context. To resolve this
issue, we propose an explicit context model by using a convolutional neural
network, which predicts whether an image region is suitable for placing a given
object or not. In our experiments, we show that our approach is able to improve
object detection, semantic and instance segmentation on the PASCAL VOC12 and
COCO datasets, with significant gains in a limited annotation scenario, i.e.
when only one category is annotated. We also show that the method is not
limited to datasets that come with expensive pixel-wise instance annotations
and can be used when only bounding boxes are available, by employing
weakly-supervised learning for instance masks approximation.Comment: Updated the experimental section. arXiv admin note: substantial text
overlap with arXiv:1807.0742
PaletteNeRF: Palette-based Color Editing for NeRFs
Neural Radiance Field (NeRF) is a powerful tool to faithfully generate novel
views for scenes with only sparse captured images. Despite its strong
capability for representing 3D scenes and their appearance, its editing ability
is very limited. In this paper, we propose a simple but effective extension of
vanilla NeRF, named PaletteNeRF, to enable efficient color editing on
NeRF-represented scenes. Motivated by recent palette-based image decomposition
works, we approximate each pixel color as a sum of palette colors modulated by
additive weights. Instead of predicting pixel colors as in vanilla NeRFs, our
method predicts additive weights. The underlying NeRF backbone could also be
replaced with more recent NeRF models such as KiloNeRF to achieve real-time
editing. Experimental results demonstrate that our method achieves efficient,
view-consistent, and artifact-free color editing on a wide range of
NeRF-represented scenes.Comment: 12 pages, 10 figure
Decomposing Single Images for Layered Photo Retouching
Photographers routinely compose multiple manipulated photos of the same scene into a single image, producing a fidelity difficult to achieve using any individual photo. Alternately, 3D artists set up rendering systems to produce layered images to isolate individual aspects of the light transport, which are composed into the final result in post-production. Regrettably, these approaches either take considerable time and effort to capture, or remain limited to synthetic scenes. In this paper, we suggest a method to decompose a single image into multiple layers that approximates effects such as shadow, diffuse illumination, albedo, and specular shading. To this end, we extend the idea of intrinsic images along two axes: first, by complementing shading and reflectance with specularity and occlusion, and second, by introducing directional dependence. We do so by training a convolutional neural network (CNN) with synthetic data. Such decompositions can then be manipulated in any off-the-shelf image manipulation software and composited back. We demonstrate the effectiveness of our decomposition on synthetic (i. e., rendered) and real data (i. e., photographs), and use them for photo manipulations, which are otherwise impossible to perform based on single images. We provide comparisons with state-of-the-art methods and also evaluate the quality of our decompositions via a user study measuring the effectiveness of the resultant photo retouching setup. Supplementary material and code are available for research use at geometry.cs.ucl.ac.uk/projects/2017/layered-retouching
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