VTubing, the practice of live streaming using virtual avatars, has gained worldwide popularity among streamers seeking to maintain anonymity. While previous research has primarily focused on the social and cultural aspects of VTubing, there is a noticeable lack of studies examining the practical challenges VTubers face in creating and operating their avatars. To address this gap, we surveyed VTubers' equipment and expanded the live-streaming design space by introducing six new dimensions related to avatar creation and control. Additionally, we conducted interviews with 16 professional VTubers to comprehensively explore their practices, strategies, and challenges throughout the VTubing process. Our findings reveal that VTubers face significant burdens compared to real-person streamers due to fragmented tools and the multi-tasking nature of VTubing, leading to unique workarounds. Finally, we summarize these challenges and propose design opportunities to improve the effectiveness and efficiency of VTubing.

As the idol industry continues to expand, agencies are leveraging live streaming and interactive chat features to broaden their fan base and boost revenue. This approach enables real-time interactions among concert participants, regardless of their physical locations. This study offers an in-depth analysis of chat dynamics observed in 17 live-streamed idol concerts through a data-driven, mixed-methods approach. Our findings indicate that chat activity peaks during specific moments, such as pre- and post-concert interactions and collective responses to idols’ cues. We categorized audience members into three distinct groups based on their chat behavior, revealing diverse interaction patterns. We identified three key factors that enhance active chat participation: event-triggered, audience-initiated, and performer-responsive chats. A user study confirmed these findings, highlighting a strong correlation between chat interaction characteristics and fans’ motivations and experiences. Based on these insights, we propose four strategic design recommendations to enhance fan engagement in live-stream chat environments during concerts.

We propose a real-time convolutional neural network (CNN) training and compression method for delivering high-quality live video even in a poor network environment. The server delivers a low-resolution video segment along with the corresponding CNN for super resolution (SR), after which the client applies the CNN to the segment in order to recover high-resolution video frames. To generate a trained CNN corresponding to a video segment in real-time, our method rapidly increases the training accuracy by promoting the overfitting property of the CNN while also using curriculum-based training. In addition, assuming that the pretrained CNN is already downloaded on the client side, we transfer only residual values between the updated and pretrained CNN parameters. These values can be quantized with low bits in real time while minimizing the amount of loss, as the distribution range is significantly narrower than that of the updated CNN. Quantitatively, our neural-enhanced adaptive live streaming pipeline (NEALS) achieves higher SR accuracy and a lower CNN compression loss rate within a constrained training time compared to the state-of-the-art CNN training and compression method. NEALS achieves 15 to 48% higher quality of the user experience compared to state-of-the-art neural-enhanced live streaming systems.

With the recent advancements in multimedia and computer graphics technology, virtual avatar concerts have become increasingly popular among both well-known celebrities and subculture-based virtual YouTubers, attracting large audiences. Although this new form of performance art can be delivered through various platforms that use different viewing formats, research on the components of enjoying virtual avatar concerts and their effects on the audience experience is lacking. This study aims to explore the effects of different viewing formats and song genres on sickness, presence, and immersion and identify preferences for enjoying virtual avatar concerts. We first analyzed data from 64 virtual avatar concert cases to identify the most commonly used viewing media, methods, and song genres. Based on our analysis, we designed and conducted a user experiment with four virtual avatar concert scenarios. The experiment compared two viewing methods (audience's point of view and director's edited view) and two song genres (dance and ballad) for 2D display and a head-mounted display. The results show that the audience's point of view and a dance song can be advantageous in increasing the immersive experience irrespective of the viewing medium. Furthermore, the participants show different expectations regarding performances according to song genres. We discuss these findings and propose guidelines to help design and conduct virtual avatar concerts effectively.

Owing to the COVID-19 pandemic, concerts are gradually being held online. Beyond live-streaming, it has recently become popular to utilize various realistic video technologies to add entertainment value and immersion to online concerts. We conducted a multi-view layout design study in a virtual reality environment with a head-mounted display to help users effectively explore and immerse themselves in multiple videos from various angles. Based on an analysis of an existing user interface for multi-view navigation and the characteristics of virtual reality, we proposed four layouts, i.e., 1) an evenly divided space, 2) an evenly divided designated space, 3) a widget type, and 4) an avatar type. We implemented a prototype by applying Korean pop concerts, where multi-view videos are the most actively utilized, and then conducted a user study to evaluate the usability and preferences of the proposed layouts. The results show that it is adequate to arrange the multi-view videos within a 60° to 110° space and on the left and right side of the main view, which is a range that the users can comfortably access. In addition, when placing multiple videos in a designated space, it is helpful to use visual effects or simple avatars to avoid visual burden being placed on the users.

We present a new interactive playback method to enhance 360° viewing experiences. Our method automatically rotates the virtual camera of a 360° panoramic video (360° video) player during interactive viewing to guide the viewer through the most important regions of the video. With this method, the viewer can watch a 360° video with minimum efforts to find important events in a scene both in interactive (e.g., HMD) and less-interactive (e.g., PC and TV) viewing environments. To estimate the importance of each viewing direction, we combine spatial and temporal saliency with cluster-based weighting. A maximum backward cumulative importance volume (MBCIV) is then constructed by accumulating this importance in the video space. During playback, which uses a forward tracing scheme through the MBCIV, the initial optimal path is found based on the viewer’s viewing direction. A smooth path is then derived using penalized curve fitting. Finally, the virtual camera is rotated to follow the path. The experiments and user studies demonstrate that our method allows the viewer to effectively enjoy 360° videos with minimum interaction efforts, or even through a non-interactive display.

PicMe is a mobile application that provides interactive on-screen guidance that helps the user take pictures of a composition that another person requires. Once the requester captures a picture of the desired composition and delivers it to the user (photographer), a 2.5D guidance system, called the virtual frame, guides the user in real-time by showing a three-dimensional composition of the target image (i.e., size and shape). In addition, according to the matching accuracy rate, we provide a small-sized target image in an inset window as feedback and edge visualization for further alignment of the detail elements. We implemented PicMe to work fully in mobile environments. We then conducted a preliminary user study to evaluate the effectiveness of PicMe compared to traditional 2D guidance methods. The results show that PicMe helps users reach their target images more accurately and quickly by giving participants more confidence in their tasks.

With the popularity of immersive display systems that fill the viewer’s field of view (FOV) entirely, demand for wide FOV content has increased. A video extrapolation technique based on reuse of existing videos is one of the most efficient ways to produce wide FOV content. Extrapolating a video poses a great challenge, however, due to the insufficient amount of cues and information that can be leveraged for the estimation of the extended region. This article introduces a novel framework that allows the extrapolation of an input video and consequently converts a conventional content into one with wide FOV. The key idea of the proposed approach is to integrate the information from all frames in the input video into each frame. Utilizing the information from all frames is crucial because it is very difficult to achieve the goal with a two-dimensional transformation based approach when parallax caused by camera motion is apparent. Warping guided by three-dimensnional scene points matches the viewpoints between the different frames. The matched frames are blended to create extended views. Various experiments demonstrate that the results of the proposed method are more visually plausible than those produced using state-of-the-art techniques.

We present a hybrid approach that segments an object by using both color and depth information obtained from views captured from a low-cost RGBD camera and sparsely-located color cameras. Our system begins with generating dense depth information of each target image by using Structure from Motion and Joint Bilateral Upsampling. We formulate the multi-view object segmentation as the Markov Random Field energy optimization on the graph constructed from the superpixels. To ensure inter-view consistency of the segmentation results between color images that have too few color features, our local mapping method generates dense inter-view geometric correspondences by using the dense depth images. Finally, the pixel-based optimization step refines the boundaries of the results obtained from the superpixel-based binary segmentation. We evaluate the validity of our method under various capture conditions such as numbers of views, rotations, and distances between cameras. We compared our method with the state-of-the-art methods that use the standard multi-view datasets. The comparison verified that the proposed method works very efficiently especially in a sparse wide-baseline capture environment.

A low-end embedded platform for Internet of Things (IoT) often suffers from a critical trade-off dilemma between security enhancement and computation overhead. We propose PUFSec, a new device fingerprint-based security architecture for IoT devices. By leveraging intrinsic hardware characteristics, we aim to design a computationally lightweight security software system architecture so that complex cryptography computation can dramatically be prohibited. We exploit the innovative idea of Public Physical Unclonable Functions (PPUFs) that fundamentally protects attackers from recovering the secret key from public gate delay information. We implement its hardware logic in a real-world FPGA board. On top of the PPUF fingerprint hardware, we present an adaptive security control mechanism consisting of adaptive key generation and key exchange protocol, which adjusts security strength depending on system load dynamics. We demonstrate that our PPUF FPGA implementation embeds distinctive variability enough to distinguish between two different PPUFs with high fidelity. We validate our PUFSec architecture by implementing necessary algorithms and protocols in a real-world IoT platform, and performing empirical evaluation in terms of computation and memory usages, proving its practical feasibility.

This paper introduces ScreenX, which is a novel movie viewing platform that enables ordinary movie theatres to become multi-projection movie theatres. This enables the general public to enjoy immersive viewing experiences. The left and right side walls are used to form surrounding screens. This surrounding display environment delivers a strong sense of immersion in general movie viewing. However, naïve display of the content on the side walls results in the appearance of distorted images according to the location of the viewer. In addition, the different dimensions in width, height, and depth among theatres may lead to different viewing experiences. Therefore, for successful deployment of this novel platform, an approach to providing similar movie viewing experiences across target theatres is presented. The proposed image representation model ensures minimum average distortion of the images displayed on the side walls when viewed from different locations. Furthermore, the proposed model assists with determining the appropriate variation of the content according to the diverse viewing environments of different theatres. The theatre suitability estimation method excludes outlier theatres that have extraordinary dimensions. In addition, the content production guidelines indicate appropriate regions to place scene elements for the side wall, depending on their importance. The experiments demonstrate that the proposed method improves the movie viewing experiences in ScreenX theatres. Finally, ScreenX and the proposed techniques are discussed with regard to various aspects and the research issues that are relevant to this movie viewing platform are summarized.

This paper presents Rich360, a novel system for creating and viewing a 360° panoramic video obtained from multiple cameras placed on a structured rig. Rich360 provides an as-rich-as-possible 360° viewing experience by effectively resolving two issues that occur in the existing pipeline. First, a deformable spherical projection surface is utilized to minimize the parallax from multiple cameras. The surface is deformed spatio-temporally according to the depth constraints estimated from the overlapping video regions. This enables fast and efficient parallax-free stitching independent of the number of views. Next, a non-uniform spherical ray sampling is performed. The density of the sampling varies depending on the importance of the image region. Finally, for interactive viewing, the non-uniformly sampled video is mapped onto a uniform viewing sphere using a UV map. This approach can preserve the richness of the input videos when the resolution of the final 360° panoramic video is smaller than the overall resolution of the input videos, which is the case for most 360° panoramic videos. We show various results from Rich360 to demonstrate the richness of the output video and the advancement in the stitching results.

High-quality depth painting for each object in a scene is a challenging task in 2D to 3D stereo conversion. One way to accurately estimate the varying depth within the object in an image is to utilize existing 3D models. Automatic pose estimation approaches based on 2D-3D feature correspondences have been proposed to obtain depth from a given 3D model. However, when the 3D model is not identical to the target object, previous methods often produce erroneous depth in the vicinity of the silhouette of the object. This paper introduces a novel 3D model-based depth estimation method that effectively produces high-quality depth information for rigid objects in a stereo conversion workflow. Given an exemplar 3D model and user correspondences, our method generates detailed depth of an object by optimizing the initial depth obtained by the application of structural fitting and silhouette matching in the image domain. The final depth is accurate up to the given 3D model, while consistent with the image. Our method was applied to various image sequences containing objects with different appearances and varying poses. The experiments show that our method can generate plausible depth information that can be utilized for high-quality 2D to 3D stereo conversion.

Abstract Accurate depth estimation is a challenging, yet essential step in the conversion of a 2D image sequence to a 3D stereo sequence. We present a novel approach to construct a temporally coherent depth map for each image in a sequence. The quality of the estimated depth is high enough for the purpose of2D to 3D stereo conversion. Our approach first combines the video sequence into a panoramic image. A user can scribble on this single panoramic image to specify depth information. The depth is then propagated to the remainder of the panoramic image. This depth map is then remapped to the original sequence and used as the initial guess for each individual depth map in the sequence. Our approach greatly simplifies the required user interaction during the assignment of the depth and allows for relatively free camera movement during the generation of a panoramic image. We demonstrate the effectiveness of our method by showing stereo converted sequences with various camera motions.

Abstract Computer graphics is one of the most efficient ways to create a stereoscopic image. The process of stereoscopic CG generation is, however, still very inefficient compared to that of monoscopic CG generation. Despite that stereo images are very similar to each other, they are rendered and manipulated independently. Additional requirements for disparity control specific to stereo images lead to even greater inefficiency. This paper proposes a method to reduce the inefficiency accompanied in the creation of a stereoscopic image. The system automatically generates an optimized single image representation of the entire visible area from both cameras. The single image can be easily manipulated with conventional techniques, as it is spatially smooth and maintains the original shapes of scene objects. In addition, a stereo image pair can be easily generated with an arbitrary disparity setting. These convenient and efficient features are achieved by the automatic generation of a stereo camera pair, robust occlusion detection with a pair of Z‐buffers, an optimization method for spatial smoothness, and stereo image pair generation with a non‐linear disparity adjustment. Experiments show that our technique dramatically improves the efficiency of stereoscopic image creation while preserving the quality of the results.