In recent years, technologies for creating aerial displays, such as drone light shows, have garnered significant attention. However, drone light shows have the disadvantage of requiring many drones and being expensive. To overcome this disadvantage, our group had proposed a cost-effective method for aerial displays using a spherical screen suspended by drones and a high-brightness projector. However, the previous methods faced several challenges in achieving large-scale, long-distance projections. This study proposes a new method for projecting onto large spherical screens at long distances by developing a new infrared marker structure and a processing method. This method uses highspeed vision to detect the position of a moving screen using the developed IR marker and project images precisely at that position to create an aerial display. The gaze direction of the high-speed camera and the projection direction of the projector was controlled with a high-speed optical gaze controller. The novel arrangement of the two IR light sources enabled dynamic projection onto larger screens at long distances. To validate the proposed system, we conducted a field experiment. In this evaluation, a large spherical display with a diameter of 2.3 meters was suspended from a drone and maneuvered at a distance of 100 meters from the projection equipment. The system successfully demonstrated stable projection onto the moving screen.
We present a lightweight and interpretable framework for analyzing anime-style facial expressions using sparse, semantically meaningful feature-line annotations. By deforming annotated contours such as eyelids, eyebrows, and lips into a canonical frontal pose, our method removes pose variation while preserving expressive differences. The canonicalized space enables consistent expression clustering and transfer across views, providing a practical foundation for stylized face normalization in anime production.
This study connects visual rhetoric with computational and quantitative methods to reveal the cultural norms embedded in film poster design across languages and regions. We constructed a large-scale dataset of over 100,000 posters from The Movie Database (TMDB), covering foreign films released between 1960 and 2024 that also had Japanese versions. Using image processing techniques, we extracted visual features such as colour, text, faces, and objects, and conducted cross-linguistic comparisons. The analysis revealed that Japanese posters display substantially more visual information than their original counterparts. By introducing quantitative and statistical perspectives to a field that has largely relied on case studies, this research provides new academic contributions.
We present a Neural Radiance Cache (NRC) integrated into the global illumination and reflection passes of a hybrid renderer targeting mobile devices. Real-time Monte Carlo rendering on mobile remains challenging due to limited compute and memory bandwidth, despite the emergence of hardware-accelerated ray tracing on mobile GPUs. Ray tracing workloads often exhibit incoherent memory access patterns that can bottleneck GPU performance. In contrast, our NRC implementation shifts this cost toward predictable, compute-centric neural inference. Our design features a fused MLP architecture implemented entirely in compute shaders, along with an efficient path-traced training pipeline decoupled from inference. While introducing more overhead than traditional radiance cache techniques, our mobile-optimized NRC achieves higher perceptual quality while maintaining real-time frame rates across a range of scenes on modern mobile hardware.
