Reconstructing realistic digital twins has become crucial as advances in mixed reality, metaverse, and robotics demand more accurate simulations for the physical world. Despite technical progress, building high-fidelity digital twins from a systematic and human-centered perspective remains underexplored. Drawing from the human processing model, we decompose human-centric reality into perception, motion, and cognition, and define a reality-preserving digital twin (RPDT) as a reconstruction integrating these dimensions. We present RealTwin, an attribute-graph-based representation and inference framework for RPDT. Leveraging the grounding capabilities of Multimodal Large Language Models (MLLMs), RealTwin chains AI tools to construct attribute graphs that faithfully encode real-world properties. We validate RealTwin through both technical evaluation, showing promising success in graph parsing and attribute inference, and a user study, assessing its applicability across diverse user groups. Enlightened by RealTwin, we discuss critical issues, including ecology, interaction space, and real-world adoption, for future end-to-end, fine-grained, and scalable digital twin reconstruction.

Target selection is a fundamental interaction in virtual reality (VR). But the act of confirming a selection, such as a button press or pinch, can disturb the tracked pose and shift the intended target, which is referred to as the Heisenberg Effect. Prior research has mainly investigated controller input. However, it remains unclear how the effect manifests in the bare-hand input and how score-based techniques may mitigate the effect in different spatial variations. To fill the gap, we conduct a within-subject study to examine the Heisenberg Effect across two input modalities (i.e., controller and hand) and two selection mechanisms (i.e., direct and score-based). Our results show that hand input is more susceptible to the Heisenberg Effect, with direct selection more influenced by target width and score-based selection more sensitive to target density. Based on previous vote-oriented technique and our temporal analysis, we introduce weighted VOTE, a history-based intention accuracy model for target voting, that reweights recent interaction intent to counteract input disturbances. Our evaluation shows the method improves selection accuracy compared to baseline techniques. Finally, we discuss future directions for adaptive selection methods.

CHI ’25, Yokohama, Japan

Older adults tend to encounter challenges when learning to use new smartphone apps due to age-related cognitive and physical changes. Compared to traditional support methods such as video tutorials, trial-and-error allows older adults to learn to use smartphone apps by making and correcting mistakes. However, it remains unknown how trial-and-error should be designed to empower older adults to use smartphone apps and how well it would work for older adults. Informed by the guidelines derived from prior work, we designed and implemented ExplorAR, an AR-based trial-and-error system that offers real-time and situated visual guidance in the augmented space around the smartphone to empower older adults to explore and correct mistakes independently. We conducted a user study with 18 older adults to compare ExplorAR with traditional video tutorials and a simplified version of ExplorAR. Results show that the AR-supported trial-and-error method enhanced older adults' learning experience by fostering deeper cognitive engagement and improving confidence in exploring unknown operations.

Real-time streaming of point cloud video, characterized by massive data volumes and high sensitivity to packet loss, remains a key challenge for immersive applications under dynamic network conditions. While connection-oriented protocols such as TCP and more modern alternatives like QUIC alleviate some transport-layer inefficiencies, including head-of-line blocking, they still retain a coarse-grained, segment-based delivery model and a centralized control loop that limit fine-grained adaptation and effective caching. We introduce INDS (Incremental Named Data Streaming), an adaptive streaming framework based on Information-Centric Networking (ICN) that rethinks delivery for hierarchical, layered media. INDS leverages the Octree structure of point cloud video and expressive content naming to support progressive, partial retrieval of enhancement layers based on consumer bandwidth and decoding capability. By combining time-windows with Group-of-Frames (GoF), INDS's naming scheme supports fine-grained in-network caching and facilitates efficient multi-user data reuse. INDS can be deployed as an overlay, remaining compatible with QUIC-based transport infrastructure as well as future Media-over-QUIC (MoQ) architectures, without requiring changes to underlying IP networks. Our prototype implementation shows up to 80% lower delay, 15-50% higher throughput, and 20-30% increased cache hit rates compared to state-of-the-art DASH-style systems. Together, these results establish INDS as a scalable, cache-friendly solution for real-time point cloud streaming under variable and lossy conditions, while its compatibility with MoQ overlays further positions it as a practical, forward-compatible architecture for emerging immersive media systems.

Inter-generational communication plays a vital role in bridging generational gaps and fostering mutual understanding. However, it remains challenging due to differences in cultural norms, communication styles, and geographical separation. While prior studies have shown Virtual Reality (VR) as a medium that fosters a relaxed atmosphere and companionship, its capacity to address the nuanced dynamics of inter-generational dialogue, such as divergent values and relational intricacies, remains limited. To address this gap, we explored the opportunities of Artificial Intelligence (AI) to support inter-generational communication in VR. We developed three technology probes (e.g., Content Generator, Communication Facilitator, and Info Assistant) in VR and employed them in a probe-based participatory design study with twelve inter-generational pairs. Our results show that AI-powered VR facilitates inter-generational communication by enhancing mutual understanding, fostering conversation fluency, and promoting active participation. We also introduce several challenges when using AI-powered VR in supporting inter-generational communication and derive design implications for future VR platforms, aiming to improve inter-generational communication.