Kwanguk Kim

사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.

Integrating motion capture technology into a beaming system enables the formation of physical synchrony (PS) at a distance. This study extends previous research conducted in face-to-face (F2F) settings to investigate whether prior PS established remotely through a beamed robot (BR) can enhance social closeness and inter-brain synchrony (IBS). The results demonstrate that prior PS via BR enhances copresence, social presence, and IBS in overall prefrontal regions. Furthermore, we found that similar patterns of prefrontal cortex activation mainly occurred in the area related to cognitive social processing. Our results highlight BR as a platform for physically mediated social engagement in remote interaction contexts that can be utilized to facilitate future applications in remote education, collaborative problem-solving, and human–robot interaction.

Human activity recognition (HAR) provided several benefits to people without disabilities. Researchers have provided public HAR datasets and developed technologies based on these datasets. However, the activities of pedestrians with mobility disabilities have not been actively investigated because related datasets do not exist. In this study, we compile an HAR dataset comprising basic activities for people with and without mobility disabilities (including activity categories of still, walking, crutches, walkers, manual wheelchairs, and electric wheelchairs). Our dataset contains sensor data from smart devices (smartphones and smartwatches) collected from 120 participants. We also provide baseline analyses of our dataset: (1) recognition tasks according to the pedestrian activities, (2) impact of sensor combinations, (3) classification models, (4) evaluation methods, and (5) combining smartphone and smartwatch sensors. The results indicate that the classification accuracies are 99.64% for the random evaluation and 98.79% for the user-independent evaluation using the best combinations. We hope that this study will expand HAR research for people with mobility disabilities and renew enthusiasm for subsequent applications related to this topic.

This study evaluated the genetic diversity and population structure of four Korean native chicken lines using 30K SNP chip data. A total of 192 birds from the four lines were analyzed to assess heterozygosity, inbreeding coefficients, linkage disequilibrium (LD), effective population size (Ne), and inter-line relationships. All lines exhibited similar levels of genetic diversity, with slightly negative inbreeding coefficient values indicating minimal inbreeding. LD decay patterns were comparable across lines, and Ne values suggested stable long-term genetic variability. Principle component analysis, population structure analysis, and phylogenetic analyses consistently showed clear separation among the four lines, with the Ogye line most distinct and the grey-brown and white line pair genetically closest. Overall, these results demonstrate that the current breeding and mating scheme effectively maintains within-line genetic diversity while preserving distinct line identities.

Although there are various studies on automatic-emotion-recognition (AER), the bodily AER is scarce compared to other emotion modalities owing to limitations of research methodology. Herein, we suggest methodologies for collecting large emotional body movement data under three factors for bodily AER dataset construction: participant expertise, motion capture devices, and emotional stimuli, and compared classification accuracy using machine learning and deep learning such as convolutional neural networks, graph convolutional networks, long short-term memory and Transformer. The first study suggests that the models trained using the non-actor dataset performed better than the other model. The second study suggests that the models trained using both marker-based-MoCap and pose-estimation performed better than Kinect-MoCap and mobile-MoCap. The third study suggests that training with both word and video stimuli performed better than picture stimuli. Considering the emotion classification accuracy and accessibility, we recommend gathering bodily AER dataset using non-actors, pose-estimation, and using either word or video stimuli. The current findings may contribute to future research methodologies for bodily emotion recognition.

We present TouchWalker, a real-time system for controlling full-body avatar locomotion using finger-walking gestures on a touchscreen. The system comprises two main components: TouchWalker-MotionNet, a neural motion generator that synthesizes full-body avatar motion on a per-frame basis from temporally sparse two-finger input, and TouchWalker-UI, a compact touch interface that interprets user touch input to avatar-relative foot positions. Unlike prior systems that rely on symbolic gesture triggers or predefined motion sequences, TouchWalker uses its neural component to generate continuous, context-aware full-body motion on a per-frame basis-including airborne phases such as running, even without input during mid-air steps-enabling more expressive and immediate interaction. To ensure accurate alignment between finger contacts and avatar motion, it employs a MoE-GRU architecture with a dedicated foot-alignment loss. We evaluate TouchWalker in a user study comparing it to a virtual joystick baseline with predefined motion across diverse locomotion tasks. Results show that TouchWalker improves users' sense of embodiment, enjoyment, and immersion.

Background: Electrical muscle stimulation (EMS) systems enhance human capabilities, such as reaction time, by inducing preemptive muscle contractions. One of the key challenges for EMS applications is preserving the user’s sense of agency, and it is defined as a subjective experience of initiating and controlling one’s actions. Prior research highlights the importance of the “sweet spot”—a balance between sense of agency and preemptive gain—for effective EMS use. However, most prior studies have focused on simplistic tasks, leaving a gap in understanding how the sweet spot functions in complex and ecological game scenarios. Moreover, the potential benefits of a personalized approach were not explored. Objective: This study examines the effects of embodied EMS on performance and sense of agency in a serious-games–based pistol-shooting scenario. Additionally, we investigated the effects of personalization in the same game scenario. Methods: Two studies were conducted. Study 1 identified the optimal EMS timing (“sweet spot”) to improve response time while preserving agency. A total of 13 participants completed a visual stimulus-response task after EMS calibration. Participants performed 150 right-button clicks on a target using an EMS-equipped mouse, with EMS timings ranging from −200 to +100 ms. An agency questionnaire followed each trial. Logistic regression was used to identify the sweet spot. Study 2 applied the findings of Study 1 to a pistol-shooting game. A total of 10 new participants were recruited. Before gameplay, individual sweet spots were measured for the individually embodied EMS condition. During the game, participants navigated to a target location, distinguished enemies from hostages, and shot enemies. After a practice session, participants completed the game under 4 counterbalanced conditions: averagely embodied EMS (used the average sweet spot value from Study 1), individually embodied EMS (used each participant’s own sweet spot value), immediate EMS (stimulation at a target onset timing), and no EMS. Results: Study 1 identified a sweet spot that significantly improved reaction time while preserving agency. Logistic analysis showed an average sweet spot of 46.8 ms and individual variability across participants. Study 2 confirmed these findings in a pistol-shooting game. Both averagely and individually embodied EMS significantly reduced reaction times compared to no EMS ( P =.03 and P =.001, respectively), without compromising agency. Individually embodied EMS yielded additional benefits. In the slower than average group, the sense of agency in the individually embodied EMS condition showed an additional benefit that was not observed in the averagely embodied EMS condition ( P =.003 and P =, respectively). Conclusions: The findings indicate that embodied EMS enhances performance in complex game scenarios while maintaining users’ sense of agency, with individualization yielding additional benefits. These results extend prior evidence to more complex game contexts and provide valuable implications for the personalized design of EMS systems in sports training, rehabilitation, and serious gaming.

Large language models (LLMs) have evolved into LLM agents that can use the user conversation context and respond according to the roles of the LLM agents. Recent studies have suggested that LLM-based agents can be used as human-like partners in social interactions. However, the role of the environmental context, particularly spatial information of user space, in the interaction between humans and LLM agents has not been explored. In this study, participants engaged in counselling conversations under three different conditions based on their spatial awareness levels. The dependent measures included copresence, trust, therapist alliances, and self-disclosure. The results suggested that participants in the condition where the LLM actively reflected spatial information generally reported higher levels of user experience. Interestingly, when the LLM actively reflected the spatial context of the user, the participants tended to describe themselves and express their emotions more. These findings suggest that spatially aware LLM agents can contribute to better social interactions between humans and LLM agents. Our findings can be used to design future augmented reality applications in the counselling, education, and healthcare industries.