Animation production workflows often involve digital colorization of line art, where small unpainted regions (“gaps”) frequently occur and remain an underexplored challenge. We conducted a formative study in Japanese animation (anime) pipelines and found that while the paint bucket tool is widely used for base coloring, tiny enclosed areas are frequently overlooked, resulting in time-consuming manual detection and filling. We introduce GapFill, a tool grounded in professional practices that reduces the effort of gap detection, zooming, and color selection. Our deep-learning method suggests appropriate fill colors by referencing surrounding regions, leveraging the flat-color nature of anime-style images. In a user study with 13 professional colorists, our system improved performance and usability in gap-filling tasks over conventional methods. The study also suggested that prediction accuracy alone is not the primary factor for usability, that appropriate colors can be contextually ambiguous, and that GapFill can complement existing tools depending on users’ trust in new AI-powered assistance.

Despite widespread use, charts remain largely inaccessible for Low-Vision Individuals (LVI). Reading charts requires viewing data points within a global context, which is difficult for LVI who may rely on magnification or experience a partial field of vision. We aim to improve exploration by providing visual access to critical context. To inform this, we conducted a formative study with five LVI. We identified four fundamental contextual elements common across chart types: axes, legend, grid lines, and the overview. We propose two pointer-based interaction methods to provide this context: Dynamic Context, a novel focus+context interaction, and Mini-map, which adapts overview+detail principles for LVI. In a study with N=22 LVI, we compared both methods and evaluated their integration to current tools. Our results show that Dynamic Context had significant positive impact on access, usability, and effort reduction; however, worsened visual load. Mini-map strengthened spatial understanding, but was less preferred for this task. We offer design insights to guide the development of future systems that support LVI with visual context while balancing visual load.

Modern web interfaces are unnecessarily complex to use as they overwhelm users with excess text and visuals unrelated to their current goals.Such interfaces can particularly impact screen reader users (SRUs), who may need to navigate content sequentially and thus spend minutes traversing irrelevant elements compared to vision users (VUs) who visually skim in seconds.We present Task Mode, a system that dynamically filters web content based on userspecified goals using large language models to identify and prioritize relevant elements while minimizing distractions.Our approach preserves page structure while offering multiple viewing modes tailored to different access needs.Our user study with 12 participants (6 VUs, 6 SRUs) demonstrates that our approach halved task completion time for SRUs while maintaining performance for VUs, decreasing the completion time gap between groups from 2x to 1.2x.11 of 12 participants wanted to use Task Mode in the future, reporting that Task Mode supported completing tasks with less effort and fewer distractions.This work demonstrates how designing new interactions simultaneously for visual and non-visual access can reduce rather than reinforce accessibility disparities in future technology created by researchers and practitioners. CCS Concepts•

Toggle (a.1) Zoom settings panel (a.2) Highlight settings panel (a) The VeasyGuide player (b) Zoomed-in view (c) VeasyGuide's personalization settings panelsFigure 1: VeasyGuide's interface includes: (a) a video player with highlighted areas (red rectangle and hand pointer), (a.1) zoom settings toggle, (a.2) highlight settings toggle, (b) a zoomed window (toggled with the Z key), and (c) zoom and highlight settings panels.While watching a video, VeasyGuide auto-highlights pointing, marking, and sketching activities.Users can zoom into highlighted portions with the Z key, adjust zoom with arrow keys, and customize highlight and zoom appearance in real time.

Humans use multiple communication channels to interact with each other. For instance, body gestures or facial expressions are commonly used to convey an intent. The use of such non-verbal cues has motivated the development of prediction models. One such approach is predicting arousal and valence (AV) from facial expressions. However, making these models accurate for human-robot interaction (HRI) settings is challenging as it requires handling multiple subjects, challenging conditions, and a wide range of facial expressions. In this paper, we propose a data augmentation (DA) technique to improve the performance of AV predictors using 3D morphable models (3DMM). We then utilize this approach in an HRI setting with a mediator robot and a group of three humans. Our augmentation method creates synthetic sequences for underrepresented values in the AV space of the SEWA dataset, which is the most comprehensive dataset with continuous AV labels. Results show that using our DA method improves the accuracy and robustness of AV prediction in realtime applications. The accuracy of our models on the SEWA dataset is 0.793 for arousal and valence.