Science communication revision requires writers to dynamically balance scientific exposition and narrative engagement - a process where writers often struggle with competing directions. Existing LLM-assisted tools help with co-writing, but offer limited support for navigating this iterative, multi-directional revision process. To address this gap, we designed Spatial Balancing, an exploratory revision environment that maps rhetorical goals and revision strategies onto a two-dimensional spatial canvas for experienced science communication creators with domain expertise but lacking formal professional training. By building a design space of communication strategies and embedding them into a spatial exploratory canvas, our system treats feedback as navigational cues rather than prescriptive judgments. Our findings show that this integrated revision environment helps writers stay focused on writing goals, reason about revision as trajectories, and explore alternatives, which supports greater metacognitive control and confidence without increasing workload. This work highlights the value of spatially externalized revision environments for supporting iterative, reflective thinking during LLM-assisted writing.

Found object drawing is a creative art form incorporating everyday objects into imaginative images, offering a refreshing and unique way to express ideas. However, for many people, creating this type of work can be challenging due to difficulties in generating creative ideas and finding suitable reference images to help translate their ideas onto paper. Based on the findings of a formative study, we propose GenFODrawing, a creativity support tool to help users create diverse found object drawings. Our system provides AI-driven textual and visual inspirations, and enhances controllability through sketch-based and box-conditioned image generation, enabling users to create personalized outputs. We conducted a user study with twelve participants to compare GenFODrawing, to a baseline condition where the participants completed the creative tasks using their own desired approaches without access to our system. The study demonstrated that GenFODrawing, enabled easier exploration of diverse ideas, greater agency and control through the creative process, and higher creativity support compared to the baseline. A further open-ended study demonstrated the system's usability and expressiveness, and all participants found the creative process engaging.

Creating interactive scenes often involves complex programming tasks. Although large language models (LLMs) like ChatGPT can generate code from natural language, their output is often error-prone, particularly when scripting interactions among multiple elements. The linear conversational structure limits the editing of individual elements, and the lack of graphical and precise control complicates visual integration. To address these issues, we integrate a context-aware modularization technique that processes textual descriptions for individual elements through separate LLM modules, with a central module managing interactions among elements. It defines a top-down structure to manage interactions, ensuring clear update logic and facilitating efficient collaboration while allowing for independent updates for each element. We design a graphical user interface, MoGraphGPT, which combines modular LLMs with enhanced graphical control to generate codes for 2D interactive scenes. It enables direct integration of graphical information and offers quick, precise control through automatically generated sliders. A comparative study with Cursor Composer shows MoGraphGPT significantly improves easiness, controllability, and performance in creating 2D interactive scenes with multiple visual elements in a coding-free manner. An ablation study validates the effectiveness of modularization, and an open-ended study demonstrates the usability and expressiveness of MoGraphGPT.

AR applications commonly contain diverse interactions among different AR contents. Creating such applications requires creators to have advanced programming skills for scripting interactive behaviors of AR contents, repeated transferring and adjustment of virtual contents from virtual to physical scenes, testing by traversing between desktop interfaces and target AR scenes, and digitalizing AR contents. Existing immersive tools for prototyping/authoring such interactions are tailored for domain-specific applications. To support programming general interactive behaviors of real object(s)/environment(s) and virtual object(s)/environment(s) for novice AR creators, we propose ProInterAR, an integrated visual programming platform to create immersive AR applications with a tablet and an AR-HMD. Users can construct interaction scenes by creating virtual contents and augmenting real contents from the view of an AR-HMD, script interactive behaviors by stacking blocks from a tablet UI, and then execute and control the interactions in the AR scene. We showcase a wide range of AR application scenarios enabled by ProInterAR, including AR game, AR teaching, sequential animation, AR information visualization, etc. Two usability studies validate that novice AR creators can easily program various desired AR applications using ProInterAR.

Text entry is a recurring task in mixed reality (MR) applications, and the ability of eyes-free text entry methods to allow users to enter text without focusing on the input device is ideal and compelling. However, existing eyes-free text entry methods leave much to be desired regarding efficiency and accuracy. In this paper, we propose a new light-occlusion text entry method in MR environment that uses dual thumb typing on a touchscreen. We design a partially visible keyboard as visual feedback to improve user performance. In addition, we optimize the underlying keyboard by collecting eyes-free typing data through a user study. The results show that our method has high typing speed, low error rate, and is very novice-friendly. After a short training period, the average typing speed of the novice group can reach 26.23 WPM (words per minute), while the average typing speed of the potential expert group can reach 30.62 WPM.

The rapid advances in technologies have brought new interaction paradigms of smart objects (e.g., digital devices) beyond digital device screens. By utilizing spatial properties, configurations, and movements of smart objects, designing spatial interaction, which is one of the emerging interaction paradigms, efficiently promotes engagement with digital content and physical facility. However, as an important phase of design, prototyping such interactions still remains challenging, since there is no ad-hoc approach for this emerging paradigm. Designers usually rely on methods that require fixed hardware setup and advanced coding skills to script and validate early-stage concepts. These requirements restrict the design process to a limited group of users in indoor scenes. To facilitate the prototyping to general usages, we aim to figure out the design difficulties and underlying needs of current design processes for spatially-aware object interactions by empirical studies. Besides, we explore the design space of the spatial interaction for smart objects and discuss the design space in an input-output spatial interaction model. Based on these findings, we present ProObjAR, an all-in-one novel prototyping system with an Augmented Reality Head Mounted Display (AR-HMD). Our system allows designers to easily obtain the spatial data of smart objects being prototyped, specify spatially-aware interactive behaviors from an input-output event triggering workflow, and test the prototyping results in situ. From the user study, we find that ProObjAR simplifies the design procedure and increases design efficiency to a large extent and thus advancing the development of spatially-aware applications in smart ecosystems.

Text entry is a frequently used task in virtual reality (VR) applications, and controller is the most common interactive device in current VR systems. However, in terms of typing speed, there is still a gap between the existing controller-based text entry techniques and using a physical keyboard in reality, so it is important to improve the efficiency of the controller-based text entry. In this paper, we introduce Flower Text Entry, a single-controller text entry method based on a newly designed flower-shaped keyboard using hand 3D translation interaction for letters selection. We conduct user studies to optimize the keyboard design and the mapping between the interaction and selection, so as to evaluate our method. The results show that our method has high typing speed, lower error rate, and is very friendly to novices compared with the state-of-the-art controller-based text entry methods. After a short training, the novice group can type at 17.65 words per minute (WPM), and the potential expert group can type at 22.97 WPM. The highest typing speed is up to 30.80 WPM achieved by a potential expert participant.

In this paper, we provide a bidirectional shadow rendering method to render shadows between real and virtual objects in the 360° videos in real time. We construct a 3D scene approximation from the current output viewpoint to approximate the real scene geometry nearby in the video. Then, we propose a ray casting based algorithm to determine the shadow regions on the virtual objects cast by the real objects. After that, we introduce an object-aware shadow mapping method to cast shadows from virtual objects to real objects. Finally, we use a shadow intensity estimation algorithm to determine the shadow intensity of virtual objects and real objects to obtain shadows consistent with the input 360° video. The experiment results prove the effectiveness of our bidirectional shadow rendering method for mixed 360° videos. Our method can generate visually realistic shadows for virtual objects and real objects in 360° video in realtime, and make virtual objects more natural to integrate with real scenes in 360° videos of the mixed reality applications.