[Subject playing with flat images: Transmedia spreadability of anime and manga character images]

Anime and manga characters are so ubiquitous in Japan that people see them anywhere, regardless of public or private. The two-dimensional flatness of line drawing allows the character images to straddle the boundaries of the worlds of works and media. The analysis of individual artworks needs to be revised to understand the transmedia spreadability of characters and our living conditions. This paper, thus, overviews the preceding discussions of anime and manga scholars on human-character relations and compares them with the theory of subject formation in film studies, whose realist tendency once emphasized the three-dimensional space and time. While psychoanalysis-influenced theories such as suture and male gaze model after one-point perspective and subordinate two-dimensionality, Thomas Lamarre formulates a de-unified perspective in relation to the multilayered image field of Japanese anime. In conjunction with the orientation of cultural and fan studies to emphasize audience agency, this article discusses the de-unified perspective on flat images which has the potential for understanding the complexity of subject formation through the interplay with two-dimensional images.

Progressively Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

Unsupervised image-to-image translation has received considerable attention due to the recent remarkable advancements in generative adversarial networks (GANs). In image-to-image translation, state-of-the-art methods use unpaired image data to learn mappings between the source and target domains. However, despite their promising results, existing approaches often fail in challenging conditions, particularly when images have various target instances and a translation task involves significant transitions in shape and visual artifacts when translating low-level information rather than high-level semantics. To tackle the problem, we propose a novel framework called Progressive Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (PRO-U-GAT-IT) for the unsupervised image-to-image translation task. In contrast to existing attention-based models that fail to handle geometric transitions between the source and target domains, our model can translate images requiring extensive and holistic changes in shape. Experimental results show the superiority of the proposed approach compared to the existing state-of-the-art models on different datasets.

Otakuism and the Appeal of Sex Robots

Social robots are becoming increasingly prevalent in everyday life and sex robots are a sub-category of especially high public interest and controversy. Starting from the concept of the otaku, a term from Japanese youth culture that describes secluded persons with a high affinity for fictional manga characters, we examine individual differences behind sex robot appeal (anime and manga fandom, interest in Japanese culture, preference for indoor activities, shyness). In an online-experiment, 261 participants read one out of three randomly assigned descriptions of future technologies (sex robot, nursing robot, genetically modified organism) and reported on their overall evaluation, eeriness, and contact/purchase intentions. Higher anime and manga fandom was associated with higher appeal for all three future technologies. For our male subsample, sex robots and GMOs stood out as shyness yielded a particularly strong relationship to contact/purchase intentions for these new technologies.