Mechanical assembly instructions are commonly delivered through static manuals or fixed-sequence animations, which limit users’ ability to seek clarification, request partial explanations, or adapt guidance to their moment-to-moment needs during physical assembly. We present AssembleIt, an interactive system that generates on-demand 3D assembly animations and verbal explanations directly from natural language user queries, without relying on pre-authored instructional content. AssembleIt automatically derives a part dependency graph from CAD geometry using an Assembly-by-Disassembly strategy and uses this representation to generate query-driven, context-aware animations at runtime, rather than following a single predefined sequence. We evaluate AssembleIt through a controlled user study with 12 participants performing physical assembly tasks using real parts, comparing on-demand, query-driven animations against a static 3D animation baseline. Results indicate that on-demand animation generation supports flexible exploration and targeted clarification during assembly, highlighting the design potential of generative, dependency-driven instructional interfaces for hands-on mechanical tasks.

Augmented Reality (AR) tutorials enhance procedural task learning by providing situated, step-by-step guidance. Yet, creating such tutorials requires AR authoring expertise, posing a significant entry barrier. To lower this barrier, we introduce ARify, an authoring system that semi-automatically transforms narrated instructional videos into AR tutorials. To guide system design, we conducted a content analysis of video tutorials and derived a design space of instructional intents, tactics, and AR representations. Building on this, ARify generates AR tutorials by integrating a vision–language model to plan tutorial structures and an AR builder to configure AR representations, and offers interfaces that allow users to refine and customize the results. A numerical study on three machine tasks and a user study with 18 participants showed that ARify achieves promising performance across task types, and allows novices to author effective AR tutorials, validating its effectiveness and usability.

Generative AI (GenAI) has significantly advanced the ease and flexibility of image creation. However, it remains a challenge to precisely control spatial compositions, including object arrangement and scene conditions. To bridge this gap, we propose Canvas3D, an interactive system leveraging a 3D engine to enable precise spatial manipulation for image generation. Upon user prompt, Canvas3D automatically converts textual descriptions into interactive objects within a 3D engine-driven virtual canvas, empowering direct and precise spatial configuration. These user-defined arrangements generate explicit spatial constraints that guide generative models in accurately reflecting user intentions in the resulting images. We conducted a closed-ended comparative study between Canvas3D and a baseline system, and an open-ended, free-form study to assess overall system usability. The results indicate that Canvas3D outperforms the baseline on spatial control, interactivity, and overall user experience.

Mixed Reality (MR) is gaining prominence in manual task skill learning due to its in-situ, embodied, and immersive experience. To teach manual tasks, current methodologies break the task into hierarchies (tasks into subtasks) and visualize not only the current subtasks but also the future ones that are causally related. We investigate the impact of visualizing causality within an MR framework on manual task skill learning. We conducted a user study with 48 participants, experimenting with how presenting tasks in hierarchical causality levels (no causality, event-level, interaction-level, and gesture-level causality) affects user comprehension and performance in a complex assembly task. The research finds that displaying all causality levels enhances user understanding and task execution, with a compromise of learning time. Based on the results, we further provide design recommendations and in-depth discussions for future manual task learning systems.

Table tennis stroke training is a critical aspect of player development. We designed a new augmented reality (AR) system, avaTTAR, for table tennis stroke training. The system provides both “on-body” (first-person view) and “detached” (third-person view) visual cues, enabling users to visualize target strokes and correct their attempts effectively with this dual perspectives setup. By employing a combination of pose estimation algorithms and IMU sensors, avaTTAR captures and reconstructs the 3D body pose and paddle orientation of users during practice, allowing real-time comparison with expert strokes. Through a user study, we affirm avaTTAR ’s capacity to amplify player experience and training results.

Utilizing everyday objects as tangible proxies for Augmented Reality (AR) provides users with haptic feedback while interacting with virtual objects. Yet, existing methods focus on the attributes of the objects, constraining the possible proxies and yielding inconsistency in user experience. Therefore, we propose Ubi-TOUCH, an AR system that assists users in seeking a wider range of tangible proxies for AR applications based on the hand-object interaction (HOI) they desire. Given the target interaction with a virtual object, the system scans the users’ vicinity and recommends object proxies with similar interactions. Upon user selection, the system simultaneously tracks and maps users’ physical HOI to the virtual HOI, adaptively optimizing object 6 DoF and the hand gesture to provide consistency between the interactions. We showcase promising use cases of Ubi-TOUCH, such as remote tutorials, AR gaming, and Smart Home control. Finally, we evaluate the performance and usability of Ubi-TOUCH with a user study.