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"Anime"
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Exploring anime and manga
\"Whether anime or manga appeared in a single hand-drawn sketch or flashed by in an animated film, the style of this art form's imagery is unlike any other. There are infinite variations in the ways that line, shape, and color can be combined to show the emotion on human faces, their personalities, lifestyles, homes, schools, cities, temples, natural surroundings, and exciting action scenes\"-- Provided by publisher.
BOOK
Sesso, simulazioni e tecnologia: una prospettiva sociologica sulle relazioni sessuali digitalmente mediate
This study analyzes the impact of digital technologies on sexuality, exploring new forms of interaction and representation. Through emblematic cases such as relationships with fictional characters and virtual girlfriends, the study highlights the emergence of complex phenomena and proposes a taxonomy to classify them from a sociological perspective: digital sex, which pertains to erotic representation through digital technologies; synthetic sex, focusing on interactivity with corporeal (physical or digital) simulacra; and virtual sex, based on the immersion of individuals in digital worlds (such as video games or metaverses).
Journal Article
A Transformer-Based Model for Super-Resolution of Anime Image
Image super-resolution (ISR) technology aims to enhance resolution and improve image quality. It is widely applied to various real-world applications related to image processing, especially in medical images, while relatively little appliedto anime image production. Furthermore, contemporary ISR tools are often based on convolutional neural networks (CNNs), while few methods attempt to use transformers that perform well in other advanced vision tasks. We propose a so-called anime image super-resolution (AISR) method based on the Swin Transformer in this work. The work was carried out in several stages. First, a shallow feature extraction approach was employed to facilitate the features map of the input image’s low-frequency information, which mainly approximates the distribution of detailed information in a spatial structure (shallow feature). Next, we applied deep feature extraction to extract the image semantic information (deep feature). Finally, the image reconstruction method combines shallow and deep features to upsample the feature size and performs sub-pixel convolution to obtain many feature map channels. The novelty of the proposal is the enhancement of the low-frequency information using a Gaussian filter and the introduction of different window sizes to replace the patch merging operations in the Swin Transformer. A high-quality anime dataset was constructed to curb the effects of the model robustness on the online regime. We trained our model on this dataset and tested the model quality. We implement anime image super-resolution tasks at different magnifications (2×, 4×, 8×). The results were compared numerically and graphically with those delivered by conventional convolutional neural network-based and transformer-based methods. We demonstrate the experiments numerically using standard peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. The series of experiments and ablation study showcase that our proposal outperforms others.
Journal Article
A study on the presentation and influence of pan-anime culture in Chinese animated films
Taking the influence of pan-anime process on the animation creation and animation industry in China as the research object, through the multi-angle analysis of the trend in the works in recent years by domestic animation creators, the study attempted to provide some advice for integrating pan-anime culture more naturally into the creation of the current domestic animation film and narrative mode and provide some help for catering to the preferences of pan-anime audience under the new pattern.
Journal Article
Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships
In this paper, we propose a hierarchical multi-modal multi-label attribute classification model for anime illustrations using a graph convolutional network (GCN). Our focus is on the challenging task of multi-label attribute classification, which requires capturing subtle features intentionally highlighted by creators of anime illustrations. To address the hierarchical nature of these attributes, we leverage hierarchical clustering and hierarchical label assignments to organize the attribute information into a hierarchical feature. The proposed GCN-based model effectively utilizes this hierarchical feature to achieve high accuracy in multi-label attribute classification. The contributions of the proposed method are as follows. Firstly, we introduce GCN to the multi-label attribute classification task of anime illustrations, enabling the capturing of more comprehensive relationships between attributes from their co-occurrence. Secondly, we capture subordinate relationships among the attributes by adopting hierarchical clustering and hierarchical label assignment. Lastly, we construct a hierarchical structure of attributes that appear more frequently in anime illustrations based on certain rules derived from previous studies, which helps to reflect the relationships between different attributes. The experimental results on multiple datasets show that the proposed method is effective and extensible by comparing it with some existing methods, including the state-of-the-art method.
Journal Article