Anhong Guo

Ruei-Che Chang, Yuxuan Liu, Lotus Zhang et al.

Image editing is an iterative process that requires precise visual evaluation and manipulation for the output to match the editing intent. However, current image editing tools do not provide accessible interaction nor sufficient feedback for blind and low vision individuals to achieve this level of control. To address this, we developed EditScribe, a prototype system that makes image editing accessible using natural language verification loops powered by large multimodal models. Using EditScribe, the user first comprehends the image content through initial general and object descriptions, then specifies edit actions using open-ended natural language prompts. EditScribe performs the image edit, and provides four types of verification feedback for the user to verify the performed edit, including a summary of visual changes, AI judgement, and updated general and object descriptions. The user can ask follow-up questions to clarify and probe into the edits or verification feedback, before performing another edit. In a study with ten blind or low-vision users, we found that EditScribe supported participants to perform and verify image edit actions non-visually. We observed different prompting strategies from participants, and their perceptions on the various types of verification feedback. Finally, we discuss the implications of leveraging natural language verification loops to make visual authoring non-visually accessible.

Lei Zhang, Jin Pan, Jacob Gettig et al.

Immersive authoring provides an intuitive medium for users to create 3D scenes via direct manipulation in Virtual Reality (VR). Recent advances in generative AI have enabled the automatic creation of realistic 3D layouts. However, it is unclear how capabilities of generative AI can be used in immersive authoring to support fluid interactions, user agency, and creativity. We introduce VRCopilot, a mixed-initiative system that integrates pre-trained generative AI models into immersive authoring to facilitate human-AI co-creation in VR. VRCopilot presents multimodal interactions to support rapid prototyping and iterations with AI, and intermediate representations such as wireframes to augment user controllability over the created content. Through a series of user studies, we evaluated the potential and challenges in manual, scaffolded, and automatic creation in immersive authoring. We found that scaffolded creation using wireframes enhanced the user agency compared to automatic creation. We also found that manual creation via multimodal specification offers the highest sense of creativity and agency.

Jaylin Herskovitz, Andi Xu, Rahaf Alharbi et al.

Existing visual assistive technologies are built for simple and common use cases, and have few avenues for blind people to customize their functionalities. Drawing from prior work on DIY assistive technology, this paper investigates end-user programming as a means for users to create and customize visual access programs to meet their unique needs. We introduce ProgramAlly, a system for creating custom filters for visual information, e.g., 'find NUMBER on BUS', leveraging three end-user programming approaches: block programming, natural language, and programming by example. To implement ProgramAlly, we designed a representation of visual filtering tasks based on scenarios encountered by blind people, and integrated a set of on-device and cloud models for generating and running these programs. In user studies with 12 blind adults, we found that participants preferred different programming modalities depending on the task, and envisioned using visual access programs to address unique accessibility challenges that are otherwise difficult with existing applications. Through ProgramAlly, we present an exploration of how blind end-users can create visual access programs to customize and control their experiences.

Ruei-Che Chang, Yuxuan Liu, Anhong Guo

Automated live visual descriptions can aid blind people in understanding their surroundings with autonomy and independence. However, providing descriptions that are rich, contextual, and just-in-time has been a long-standing challenge in accessibility. In this work, we develop WorldScribe, a system that generates automated live real-world visual descriptions that are customizable and adaptive to users' contexts: (i) WorldScribe's descriptions are tailored to users' intents and prioritized based on semantic relevance. (ii) WorldScribe is adaptive to visual contexts, e.g., providing consecutively succinct descriptions for dynamic scenes, while presenting longer and detailed ones for stable settings. (iii) WorldScribe is adaptive to sound contexts, e.g., increasing volume in noisy environments, or pausing when conversations start. Powered by a suite of vision, language, and sound recognition models, WorldScribe introduces a description generation pipeline that balances the tradeoffs between their richness and latency to support real-time use. The design of WorldScribe is informed by prior work on providing visual descriptions and a formative study with blind participants. Our user study and subsequent pipeline evaluation show that WorldScribe can provide real-time and fairly accurate visual descriptions to facilitate environment understanding that is adaptive and customized to users' contexts. Finally, we discuss the implications and further steps toward making live visual descriptions more context-aware and humanized.

Yayuan Li, Chenglin Li, Jingying Wang et al.

Instructional videos are the dominant medium for learning physical tasks, yet they rarely match the user's real-world visual context. Motor simulation and cognitive load theories predict this mismatch should matter, but we do not know (1) how much it could affect task completion, (2) which visual attributes are responsible, and (3) how users experience it. We conduct two complementary studies (56 participants, 86+ hours, four first-aid and culinary tasks) in which we use Wizard-of-Oz recordings to control the degree of visual alignment in instructional videos. In Study 1 (N=16), we prepare In-Context instructional videos (ICON) -- fully aligned with the user's visual perception -- to compare against business-as-usual Internet videos. ICON yields statistically significant improvements: 11.1% higher completion quality and 15.5% faster completion. Qualitative analysis reveals four visual context attributes responsible for the effect: Task Object Intrinsics, Task Object State, Environmental Context, and Observational Context. Study 2 (N=40) ablates each attribute by systematically misaligning one at a time from an otherwise fully aligned video, confirming all four produce consistent degradation. However, we find users fail to perceive the effect of single-attribute misalignment on task performance despite clear drops in objective measurement. Visual context misalignment is substantial, decomposable, and invisible to the user. These findings help understand the effect of visual context mismatch and how we should evaluate instructional videos for physical task guidance.

Chen Liang, Xirui Jiang, Naihao Deng et al.

AI agents operating on user interfaces must understand how interfaces communicate state and feedback to act reliably. As a core communicative modality, animations are increasingly used in modern interfaces, serving critical functional purposes beyond mere aesthetics. Thus, understanding UI animation is essential for comprehensive interface interpretation. However, recent studies of Vision Language Models (VLMs) for UI understanding have focused primarily on static screenshots, leaving it unclear how well these models handle dynamic UI animations. To address this gap, we created AniMINT, a novel dataset of 300 densely annotated UI animation videos. We systematically evaluate state-of-the-art VLMs on UI animation understanding, including their abilities to perceive the animation effects, identify animation purposes, and interpret animation meaning. Our results show that VLMs can reliably detect primitive motion. However, their high-level animation interpretation remains inconsistent, with substantial gaps relative to human performance. Finally, we use Motion, Context, and Perceptual Cues (MCPC) to probe factors affecting VLM performance, revealing key bottlenecks and directions for future improvement.

Ruei-Che Chang, Xirui Jiang, Rosiana Natalie et al.

Real-world environments evolve continuously, yet blind and low-vision (BLV) individuals often have limited access to understanding how they change over time. Unexpected or relocated objects, layout modifications, and content updates (e.g., price changes) can introduce safety risks and cognitive burden. While existing visual assistive technologies can describe immediate surroundings, they operate as one-off interactions and lack mechanisms to surface meaningful changes across revisits. Informed by a survey of 33 BLV individuals, we develop StateScribe, a system that supports accessible awareness of real-world changes across revisits. StateScribe employs a dual-layer memory architecture that integrates episodic scene memory and object-centric temporal memory to enable scalable and structured change tracking. It provides both live descriptions of the current scene, and descriptions of what has changed, when and where it occurred across revisits, such as "The shop on your right has a "CLOSED" sign; it was open at this time last week.'' Our evaluation shows that StateScribe maintains high accuracy (F1-score=83.1%) across 11 revisits, while remaining low-latency (mean<1.54s) and memory-efficient (<54MB) across 110 revisits. A user study with nine BLV participants demonstrates that StateScribe improves change awareness across revisits in three real-world locations. Finally, we discuss implications for long-term AI-assisted companions that support broader change observation using multimodal sensing, extend beyond changes to other memory capabilities, and adapt to individual users, intents, and contexts.

Ruiping Liu, Jiaming Zhang, Angela Schön et al.

Searching for objects in unfamiliar scenarios is a challenging task for blind people. It involves specifying the target object, detecting it, and then gathering detailed information according to the user's intent. However, existing description- and detection-based assistive technologies do not sufficiently support the multifaceted nature of interactive object search tasks. We present ObjectFinder, an open-vocabulary wearable assistive system for interactive object search by blind people. ObjectFinder allows users to query target objects using flexible wording. Once the target object is detected, it provides egocentric localization information in real-time, including distance and direction. Users can then initiate different branches to gather detailed information based on their intent towards the target object, such as navigating to it or perceiving its surroundings. ObjectFinder is powered by a seamless combination of open-vocabulary models, namely an open-vocabulary object detector and a multimodal large language model. The ObjectFinder design concept and its development were carried out in collaboration with a blind co-designer. To evaluate ObjectFinder, we conducted an exploratory user study with eight blind participants. We compared ObjectFinder to BeMyAI and Google Lookout, popular description- and detection-based assistive applications. Our findings indicate that most participants felt more independent with ObjectFinder and preferred it for object search, as it enhanced scene context gathering and navigation, and allowed for active target identification. Finally, we discuss the implications for future assistive systems to support interactive object search.

Rosiana Natalie, Wenqian Xu, Ruei-Che Chang et al.

Advances in vision language models (VLMs) have enabled the simulation of general human behavior through their reasoning and problem solving capabilities. However, prior research has not investigated such simulation capabilities in the accessibility domain. In this paper, we evaluate the extent to which VLMs can simulate the vision perception of low vision individuals when interpreting images. We first compile a benchmark dataset through a survey study with 40 low vision participants, collecting their brief and detailed vision information and both open-ended and multiple-choice image perception and recognition responses to up to 25 images. Using these responses, we construct prompts for VLMs (GPT-4o) to create simulated agents of each participant, varying the included information on vision information and example image responses. We evaluate the agreement between VLM-generated responses and participants' original answers. Our results indicate that VLMs tend to infer beyond the specified vision ability when given minimal prompts, resulting in low agreement (0.59). The agreement between the agent' and participants' responses remains low when only either the vision information (0.59) or example image responses (0.59) are provided, whereas a combination of both significantly increase the agreement (0.70, p < 0.0001). Notably, a single example combining both open-ended and multiple-choice responses, offers significant performance improvements over either alone (p < 0.0001), while additional examples provided minimal benefits (p > 0.05).

Ruei-Che Chang, Rosiana Natalie, Wenqian Xu et al.

Recent advancements in large multimodal models have provided blind or visually impaired (BVI) individuals with new capabilities to interpret and engage with the real world through interactive systems that utilize live video feeds. However, the potential benefits and challenges of such capabilities to support diverse real-world assistive tasks remain unclear. In this paper, we present findings from an exploratory study with eight BVI participants. Participants used ChatGPT's Advanced Voice with Video, a state-of-the-art live video AI released in late 2024, in various real-world scenarios, from locating objects to recognizing visual landmarks, across unfamiliar indoor and outdoor environments. Our findings indicate that current live video AI effectively provides guidance and answers for static visual scenes but falls short in delivering essential live descriptions required in dynamic situations. Despite inaccuracies in spatial and distance information, participants leveraged the provided visual information to supplement their mobility strategies. Although the system was perceived as human-like due to high-quality voice interactions, assumptions about users' visual abilities, hallucinations, generic responses, and a tendency towards sycophancy led to confusion, distrust, and potential risks for BVI users. Based on the results, we discuss implications for assistive video AI agents, including incorporating additional sensing capabilities for real-world use, determining appropriate intervention timing beyond turn-taking interactions, and addressing ecological and safety concerns.

Solon Barocas, Anhong Guo, Ece Kamar et al.

Disaggregated evaluations of AI systems, in which system performance is assessed and reported separately for different groups of people, are conceptually simple. However, their design involves a variety of choices. Some of these choices influence the results that will be obtained, and thus the conclusions that can be drawn; others influence the impacts -- both beneficial and harmful -- that a disaggregated evaluation will have on people, including the people whose data is used to conduct the evaluation. We argue that a deeper understanding of these choices will enable researchers and practitioners to design careful and conclusive disaggregated evaluations. We also argue that better documentation of these choices, along with the underlying considerations and tradeoffs that have been made, will help others when interpreting an evaluation's results and conclusions.

Jaylin Herskovitz, Jason Wu, Samuel White et al.

Augmented Reality (AR) technology creates new immersive experiences in entertainment, games, education, retail, and social media. AR content is often primarily visual and it is challenging to enable access to it non-visually due to the mix of virtual and real-world content. In this paper, we identify common constituent tasks in AR by analyzing existing mobile AR applications for iOS, and characterize the design space of tasks that require accessible alternatives. For each of the major task categories, we create prototype accessible alternatives that we evaluate in a study with 10 blind participants to explore their perceptions of accessible AR. Our study demonstrates that these prototypes make AR possible to use for blind users and reveals a number of insights to move forward. We believe our work sets forth not only exemplars for developers to create accessible AR applications, but also a roadmap for future research to make AR comprehensively accessible.

Anhong Guo, Junhan Kong, Michael Rivera et al.

Blind people frequently encounter inaccessible dynamic touchscreens in their everyday lives that are difficult, frustrating, and often impossible to use independently. Touchscreens are often the only way to control everything from coffee machines and payment terminals, to subway ticket machines and in-flight entertainment systems. Interacting with dynamic touchscreens is difficult non-visually because the visual user interfaces change, interactions often occur over multiple different screens, and it is easy to accidentally trigger interface actions while exploring the screen. To solve these problems, we introduce StateLens - a three-part reverse engineering solution that makes existing dynamic touchscreens accessible. First, StateLens reverse engineers the underlying state diagrams of existing interfaces using point-of-view videos found online or taken by users using a hybrid crowd-computer vision pipeline. Second, using the state diagrams, StateLens automatically generates conversational agents to guide blind users through specifying the tasks that the interface can perform, allowing the StateLens iOS application to provide interactive guidance and feedback so that blind users can access the interface. Finally, a set of 3D-printed accessories enable blind people to explore capacitive touchscreens without the risk of triggering accidental touches on the interface. Our technical evaluation shows that StateLens can accurately reconstruct interfaces from stationary, hand-held, and web videos; and, a user study of the complete system demonstrates that StateLens successfully enables blind users to access otherwise inaccessible dynamic touchscreens.

Anhong Guo, Ilter Canberk, Hannah Murphy et al.

We introduce Blocks, a mobile application that enables people to co-create AR structures that persist in the physical environment. Using Blocks, end users can collaborate synchronously or asynchronously, whether they are colocated or remote. Additionally, the AR structures can be tied to a physical location or can be accessed from anywhere. We evaluated how people used Blocks through a series of lab and field deployment studies with over 160 participants, and explored the interplay between two collaborative dimensions: space and time. We found that participants preferred creating structures synchronously with colocated collaborators. Additionally, they were most active when they created structures that were not restricted by time or place. Unlike most of today's AR experiences, which focus on content consumption, this work outlines new design opportunities for persistent and collaborative AR experiences that empower anyone to collaborate and create AR content.

Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan et al.

AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.

Danna Gurari, Qing Li, Abigale J. Stangl et al.

The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of over 31,000 visual questions originating from blind people who each took a picture using a mobile phone and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. VizWiz differs from the many existing VQA datasets because (1) images are captured by blind photographers and so are often poor quality, (2) questions are spoken and so are more conversational, and (3) often visual questions cannot be answered. Evaluation of modern algorithms for answering visual questions and deciding if a visual question is answerable reveals that VizWiz is a challenging dataset. We introduce this dataset to encourage a larger community to develop more generalized algorithms that can assist blind people.

Michael Nebeling, Anhong Guo, Kyle Murray et al.

In this paper we introduce a paradigm for completing complex tasks from wearable devices by leveraging crowdsourcing, and demonstrate its validity for academic writing. We explore this paradigm using a collaborative authoring system, called WearWrite, which is designed to enable authors and crowd workers to work together using an Android smartwatch and Google Docs to produce academic papers, including this one. WearWrite allows expert authors who do not have access to large devices to contribute bits of expertise and big picture direction from their watch, while freeing them of the obligation of integrating their contributions into the overall document. Crowd workers on desktop computers actually write the document. We used this approach to write several simple papers, and found it was effective at producing reasonable drafts. However, the workers often needed more structure and the authors more context. WearWrite addresses these issues by focusing workers on specific tasks and providing select context to authors on the watch. We demonstrate the system's feasibility by writing this paper using it.