Image Collage on Arbitrary Shape via Shape-Aware Slicing and
Optimization
- URL: http://arxiv.org/abs/2401.02435v1
- Date: Fri, 17 Nov 2023 09:41:30 GMT
- Title: Image Collage on Arbitrary Shape via Shape-Aware Slicing and
Optimization
- Authors: Dong-Yi Wu, Thi-Ngoc-Hanh Le, Sheng-Yi Yao, Yun-Chen Lin, and Tong-Yee
Lee
- Abstract summary: We present a shape slicing algorithm and an optimization scheme that can create image collages of arbitrary shapes.
Shape-Aware Slicing, which is designed specifically for irregular shapes, takes human perception and shape structure into account to generate visually pleasing partitions.
- Score: 6.233023267175408
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image collage is a very useful tool for visualizing an image collection. Most
of the existing methods and commercial applications for generating image
collages are designed on simple shapes, such as rectangular and circular
layouts. This greatly limits the use of image collages in some artistic and
creative settings. Although there are some methods that can generate
irregularly-shaped image collages, they often suffer from severe image
overlapping and excessive blank space. This prevents such methods from being
effective information communication tools. In this paper, we present a shape
slicing algorithm and an optimization scheme that can create image collages of
arbitrary shapes in an informative and visually pleasing manner given an input
shape and an image collection. To overcome the challenge of irregular shapes,
we propose a novel algorithm, called Shape-Aware Slicing, which partitions the
input shape into cells based on medial axis and binary slicing tree.
Shape-Aware Slicing, which is designed specifically for irregular shapes, takes
human perception and shape structure into account to generate visually pleasing
partitions. Then, the layout is optimized by analyzing input images with the
goal of maximizing the total salient regions of the images. To evaluate our
method, we conduct extensive experiments and compare our results against
previous work. The evaluations show that our proposed algorithm can efficiently
arrange image collections on irregular shapes and create visually superior
results than prior work and existing commercial tools.
Related papers
- Generative Photomontage [40.49579203394384]
We propose a framework for creating the desired image by compositing it from various parts of generated images.
We let users select desired parts from the generated results using a brush stroke interface.
We show compelling results for each application and demonstrate that our method outperforms existing image blending methods.
arXiv Detail & Related papers (2024-08-13T17:59:51Z) - Block and Detail: Scaffolding Sketch-to-Image Generation [65.56590359051634]
We introduce a novel sketch-to-image tool that aligns with the iterative refinement process of artists.
Our tool lets users sketch blocking strokes to coarsely represent the placement and form of objects and detail strokes to refine their shape and silhouettes.
We develop a two-pass algorithm for generating high-fidelity images from such sketches at any point in the iterative process.
arXiv Detail & Related papers (2024-02-28T07:09:31Z) - Optimize and Reduce: A Top-Down Approach for Image Vectorization [12.998637003026273]
We propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic.
O&R aims to attain a compact representation of input images by iteratively optimizing B'ezier curve parameters.
We demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes.
arXiv Detail & Related papers (2023-12-18T16:41:03Z) - Neural Collage Transfer: Artistic Reconstruction via Material
Manipulation [20.72219392904935]
Collage is a creative art form that uses diverse material scraps as a base unit to compose a single image.
pixel-wise generation techniques can reproduce a target image in collage style, but it is not a suitable method due to the solid stroke-by-stroke nature of the collage form.
We propose a method for learning to make collages via reinforcement learning without the need for demonstrations or collage artwork data.
arXiv Detail & Related papers (2023-11-03T19:10:37Z) - Geometrically Consistent Partial Shape Matching [50.29468769172704]
Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics.
An often neglected but essential property of matching geometrics is consistency.
We propose a novel integer linear programming partial shape matching formulation.
arXiv Detail & Related papers (2023-09-10T12:21:42Z) - Localizing Object-level Shape Variations with Text-to-Image Diffusion
Models [60.422435066544814]
We present a technique to generate a collection of images that depicts variations in the shape of a specific object.
A particular challenge when generating object variations is accurately localizing the manipulation applied over the object's shape.
To localize the image-space operation, we present two techniques that use the self-attention layers in conjunction with the cross-attention layers.
arXiv Detail & Related papers (2023-03-20T17:45:08Z) - Parallax-Tolerant Unsupervised Deep Image Stitching [57.76737888499145]
We propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique.
First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion.
To further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks.
arXiv Detail & Related papers (2023-02-16T10:40:55Z) - ShaRF: Shape-conditioned Radiance Fields from a Single View [54.39347002226309]
We present a method for estimating neural scenes representations of objects given only a single image.
The core of our method is the estimation of a geometric scaffold for the object.
We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.
arXiv Detail & Related papers (2021-02-17T16:40:28Z) - Learning to Caricature via Semantic Shape Transform [95.25116681761142]
We propose an algorithm based on a semantic shape transform to produce shape exaggerations.
We show that the proposed framework is able to render visually pleasing shape exaggerations while maintaining their facial structures.
arXiv Detail & Related papers (2020-08-12T03:41:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.