We propose a novel object-mediated hand interaction system that enables real-time operation with everyday objects on wearable augmented reality (AR) devices. Despite recent advances, both commercial and academic hand interaction techniques remain constrained, typically requiring external hardware or depending exclusively on bare-hand gestures. Motivated by these constraints, we developed an offloading framework that integrates a high-fidelity transformer-based 3D hand reconstruction model with a dynamic gesture recognition network powered by gated recurrent units (GRU). This architecture ensures stable and accurate gesture recognition even during interaction with physical objects. To evaluate its quantitative performance, we collected a custom dataset based on a predefined gesture set, achieving 93.0% accuracy in 5-fold cross-validation. The complete system implemented on Microsoft HoloLens 2 operates at a real-time framerate, and we further analyze the latency of each step in our framework. Through this interaction paradigm, users can experience immersive and intuitive AR in everyday environments with minimal disruption to natural action behavior. Our projects are available at https://github.com/kaist-uvrlab/UnifiedHOInteraction.

We propose a method for generating task breakpoints based on an Origin-Centric Graph (OCG) to segment goal-oriented activity recordings into task units for adaptive playback in Virtual Reality (VR) environments. With the development of Augmented Reality (AR)/VR head-mounted displays (HMDs), research on adaptive tutorials and authoring tools has become active, but existing task segmentation methods mainly rely on manual annotation or are restricted to 2D video which limits their applicability to 3D VR contexts. In our approach, assembly scenarios with clearly defined task boundaries are recorded using a structured spatio-temporal scene graph (STSG), and the OCG is employed to track changes in the central object and the formation of new groups, thereby generating task breakpoints automatically. A user study collected user-perceived task breakpoints to establish ground truth (GT), and comparison with the algorithm-detected breakpoints demonstrated high agreement and confirmed accuracy in supporting adaptive playback. The proposed task segmentation method provides a foundation for dynamically adjusting VR playback according to user proficiency and progress, with potential for extension into automatic timeline segmentation systems for diverse VR recordings.

We propose Int3DNet, a scene-aware network that predicts 3D intention areas directly from scene geometry and head-hand motion cues, enabling robust human intention prediction without explicit object-level perception. In Mixed Reality (MR), intention prediction is critical as it enables the system to anticipate user actions and respond proactively, reducing interaction delays and ensuring seamless user experiences. Our method employs a cross attention fusion of sparse motion cues and scene point clouds, offering a novel approach that directly interprets the user's spatial intention within the scene. We evaluated Int3DNet on MoGaze and CIRCLE datasets, which are public datasets for full-body human-scene interactions, showing consistent performance across time horizons of up to 1500 ms and outperforming the baselines, even in diverse and unseen scenes. Moreover, we demonstrate the usability of proposed method through a demonstration of efficient visual question answering (VQA) based on intention areas. Int3DNet provides reliable 3D intention areas derived from head-hand motion and scene geometry, thus enabling seamless interaction between humans and MR systems through proactive processing of intention areas.

We propose a novel crowd data-driven optimization approach for artwork placement in virtual exhibitions. With the emerging concept of Metaverse, a multitude of users can engage with content contemporaneously in virtual exhibitions. Yet, few studies have suggested a method to resolve crowd density concentration in multiuser Mixed Reality (MR) and Virtual Reality (VR) environments. In this study, our approach leverages crowd data engaged with artworks to predict optimal placement of artworks to distribute crowd density in virtual exhibitions, prior to exhibition planning. To investigate the requirement and validity of our approach, we conducted focus group interviews and an artwork relocation experiment as preliminary studies. In the generation of solution scenes for optimal placement, our optimizer adaptively scrutinizes placeable areas with considerations of crowd density distribution, scene rationality, and artwork similarity. Through a performance comparison analysis between optimization results, we confirmed that the optimizer successfully fulfilled the intended objectives with respect to the design considerations, resolving practical scenarios in exhibition planning.