Sketching provides an intuitive way to convey dynamic intent in animation authoring (i.e., how elements change over time and space), making it a natural medium for automatic content creation. Yet existing approaches often constrain sketches to fixed command tokens or predefined visual forms, overlooking their free-form nature and the central role of humans in shaping intention. To address this, we introduce an interaction paradigm where users convey dynamic intent to a vision–language model via free-form sketching, instantiated here in a sketch storyboard to motion graphics workflow. We implement an interface and improve it through a three-stage study with 24 participants. The study shows how sketches convey motion with minimal input, how their inherent ambiguity requires users to be involved for clarification, and how sketches can visually guide video refinement. Our findings reveal the potential of sketch–AI interaction to bridge the gap between intention and outcome, and demonstrate its applicability to 3D animation and video generation.

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.

Remote collaborative design has become increasingly popular, but current design tools often overlook the importance of contextual communication during synchronized design activities, which is critical for understanding the rationale and decisions behind design choices. In this paper, we introduce DesignMemo, a proof-of-concept system that integrates the verbal context of remote discussions into visual design history tracking. The system automatically labels the visual elements with an annotation, which is linked to a certain transcript of the meeting, so that the user can easily recall the context of the visual design by clicking the element. The system also integrates an LLM agent for annotation-oriented summarization based on global context tracking, so users can quickly follow the rationale of the design without reading the lengthy transcript. Our user study with 24 participants suggests that the ability to track communication context makes the iterative design process smoother and more efficient.

In recent years, various head-worn virtual reality (VR) techniques have emerged to enhance object selection for occluded or distant targets. However, many approaches focus solely on ray-casting inputs, restricting their use with other input methods, such as bare hands. Additionally, some techniques speed up selection by changing the user's perspective or modifying the scene context, which may complicate interactions when users plan to resume or manipulate the scene afterward. To address these challenges, we present FocalSelect, a heuristic selection technique that builds 3D disambiguation through head-hand coordination and scoring-based functions. Our interaction design adheres to the principle that the intended selection range is a small sector of the headset's viewing frustum, allowing optimal targets to be identified within this scope. We also introduce a density-aware adjustable occlusion plane for effective depth culling of rendered objects. Two experiments are conducted to assess the adaptability of FocalSelect across different input modalities and its performance against five selection techniques. The results indicate that FocalSelect enhances selection experiences in occluded and remote scenarios while preserving the spatial context among objects. This preservation helps maintain users' understanding of the original scene and facilitates further manipulation. We also explore potential applications and enhancements to demonstrate more practical implementations of FocalSelect.

We introduce AniCraft, a mixed reality system for prototyping 3D character animation using physical proxies crafted from everyday objects. Unlike existing methods that require specialized equipment to support the use of physical proxies, AniCraft only requires affordable markers, webcams, and daily accessible objects and materials. AniCraft allows creators to prototype character animations through three key stages: selection of virtual characters, fabrication of physical proxies, and manipulation of these proxies to animate the characters. This authoring workflow is underpinned by diverse physical proxies, manipulation types, and mapping strategies, which ease the process of posing virtual characters and mapping user interactions with physical proxies to animated movements of virtual characters. We provide a range of cases and potential applications to demonstrate how diverse physical proxies can inspire user creativity. User experiments show that our system can outperform traditional animation methods for rapid prototyping. Furthermore, we provide insights into the benefits and usage patterns of different materials, which lead to design implications for future research.

This paper exploits ubiquitous desktop interaction data as an input source for generating virtual reality (VR) interaction data, which can benefit tasks like user behavior analysis and experience enhancement. Time-varying stroke gestures are selected as the primary focus because of their prevalence across various applications and their diverse patterns. The commonalities (e.g., features like velocity and curvature) between desktop and VR strokes allow the generation of additional dimensions (e.g., z vectors) in VR strokes. However, distribution shifts exist between different interaction environments (i.e., desktop vs. VR), and within the same interaction environment for different strokes by various users, making it challenging to build models capable of generalizing to unseen distributions. To address the challenges, we formulate the problem of generating VR strokes from desktop strokes as a conditional time series generation problem, aiming to learn representations that are capable of handling out-of-distribution data. We propose a novel architecture based on conditional generative adversarial networks, with the generator encompassing three steps: discretizing the output space, characterizing latent distributions, and learning conditional domain-invariant representations. We evaluate the effectiveness of our methods by comparing them with state-of-the-art time series generation models and conducting ablation studies. We further illustrate the applicability of the enriched VR datasets through two applications: VR stroke classification and stroke prediction.

Given a large number of applications and complex processing procedures, how to efficiently shift and schedule tax officers to provide good services to taxpayers is now receiving more attention from tax authorities. The availability of historical application data makes it possible for tax managers to shift and schedule staff with data support, but it is unclear how to properly leverage the historical data. To investigate the problem, this study adopts a user-centered design approach. We first collect user requirements by conducting interviews with tax managers and characterize their requirements of shifting and scheduling into time series prediction and resource scheduling problems. Then, we propose Tax-Scheduler, an interactive visualization system with a time-series prediction algorithm and genetic algorithm to support staff shifting and scheduling in the tax scenarios. To evaluate the effectiveness of the system and understand how non-technical tax managers react to the system with advanced algorithms and visualizations, we conduct user interviews with tax managers and distill several implications for future system design.