129,249 research outputs found
Structure Preserving regularizer for Neural Style Transfer
The aim of the project is to generate an image in the style of the image by a well-known artist. The experiment will use artificial neural networks to transfer the style of one image onto another. In Computer Vision context: capturing the content invariant that is the style of an image and applying it on the content of another image.
Initially captures the tensors that we need from the content and style image and then we pass the input image which will initially be an image with noise and our algorithm will try to minimize the loss between the input and content image and that between input and style image thus capturing the essence of both the images into one. The traditional method of style transfer generated image has an artistic effect that is the model successfully capture the style of the image but does not preserve the structural content of the image. The proposed method uses a segmented version of images to faithfully transfer the style to semantic similar content. Also, a regularizer term modified in loss function that helps in avoiding style spill over and have photographic results
All-to-key Attention for Arbitrary Style Transfer
Attention-based arbitrary style transfer studies have shown promising
performance in synthesizing vivid local style details. They typically use the
all-to-all attention mechanism -- each position of content features is fully
matched to all positions of style features. However, all-to-all attention tends
to generate distorted style patterns and has quadratic complexity, limiting the
effectiveness and efficiency of arbitrary style transfer. In this paper, we
propose a novel all-to-key attention mechanism -- each position of content
features is matched to stable key positions of style features -- that is more
in line with the characteristics of style transfer. Specifically, it integrates
two newly proposed attention forms: distributed and progressive attention.
Distributed attention assigns attention to key style representations that
depict the style distribution of local regions; Progressive attention pays
attention from coarse-grained regions to fine-grained key positions. The
resultant module, dubbed StyA2K, shows extraordinary performance in preserving
the semantic structure and rendering consistent style patterns. Qualitative and
quantitative comparisons with state-of-the-art methods demonstrate the superior
performance of our approach
Depth-aware Neural Style Transfer using Instance Normalization
Neural Style Transfer (NST) is concerned with the artistic stylization of
visual media. It can be described as the process of transferring the style of
an artistic image onto an ordinary photograph. Recently, a number of studies
have considered the enhancement of the depth-preserving capabilities of the NST
algorithms to address the undesired effects that occur when the input content
images include numerous objects at various depths. Our approach uses a deep
residual convolutional network with instance normalization layers that utilizes
an advanced depth prediction network to integrate depth preservation as an
additional loss function to content and style. We demonstrate results that are
effective in retaining the depth and global structure of content images. Three
different evaluation processes show that our system is capable of preserving
the structure of the stylized results while exhibiting style-capture
capabilities and aesthetic qualities comparable or superior to state-of-the-art
methods. Project page:
https://ioannoue.github.io/depth-aware-nst-using-in.html.Comment: 8 pages, 8 figures, Computer Graphics & Visual Computing (CGVC) 202
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models
Image-based fashion design with AI techniques has attracted increasing
attention in recent years. We focus on a new fashion design task, where we aim
to transfer a reference appearance image onto a clothing image while preserving
the structure of the clothing image. It is a challenging task since there are
no reference images available for the newly designed output fashion images.
Although diffusion-based image translation or neural style transfer (NST) has
enabled flexible style transfer, it is often difficult to maintain the original
structure of the image realistically during the reverse diffusion, especially
when the referenced appearance image greatly differs from the common clothing
appearance. To tackle this issue, we present a novel diffusion model-based
unsupervised structure-aware transfer method to semantically generate new
clothes from a given clothing image and a reference appearance image. In
specific, we decouple the foreground clothing with automatically generated
semantic masks by conditioned labels. And the mask is further used as guidance
in the denoising process to preserve the structure information. Moreover, we
use the pre-trained vision Transformer (ViT) for both appearance and structure
guidance. Our experimental results show that the proposed method outperforms
state-of-the-art baseline models, generating more realistic images in the
fashion design task. Code and demo can be found at
https://github.com/Rem105-210/DiffFashion
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