Videos make exercise instruction widely available, but they rely on visual demonstrations that blind and low vision (BLV) learners cannot see. While audio descriptions (AD) can make videos accessible, describing movements remains challenging as the AD must convey what to do (mechanics, location, orientation) and how to do it (speed, fluidity, timing). Prior work thus used multimodal instruction to support BLV learners with individual simple movements. However, it is unclear how these approaches scale to dance instruction with unique, complex movements and precise timing constraints. To inform accessible asynchronous dance instruction systems, we conducted three co-design workshops (N=28) with BLV dancers, instructors, and experts in sound, haptics, and AD. Participants designed 8 systems revealing common themes: staged learning to dissect routines, crafting vocabularies for movements, and selectively using modalities—narration for movement structure, sound for expression, and haptics for spatial cues. We conclude with design implications to make learning dance accessible.

Music shapes the tone of videos, yet creators find it hard to find soundtracks that match their video’s mood and narrative. Recent text-to-music models let creators generate music from text prompts, but our formative study (N=8) shows creators struggle to construct diverse prompts, quickly review and compare tracks, and understand their impact on the video. We present VidTune, a system that supports soundtrack creation by generating diverse music options from a creator’s prompt and producing contextual thumbnails for rapid review. VidTune extracts representative video subjects to ground thumbnails in context, maps each track’s valence and energy onto visual cues like color and brightness, and depicts prominent genres and instruments. Creators can refine tracks with natural language edits, which VidTune expands into new generations. In a controlled user study (N=12) and an exploratory case study (N=6), participants found VidTune helpful for efficiently reviewing and comparing music options and described the process as playful and enriching.

To make an engaging video, people sequence interesting moments and add visuals such as B-rolls or text. While video editing requires time and effort, AI has recently shown strong potential to make editing easier through suggestions and automation. A key strength of generative models is their ability to quickly generate multiple variations, but when provided with many alternatives, creators struggle to compare them to find the best fit. We propose VideoDiff, an AI video editing tool designed for editing with alternatives. With VideoDiff, creators can generate and review multiple AI recommendations for each editing process: creating a rough cut, inserting B-rolls, and adding text effects. VideoDiff simplifies comparisons by aligning videos and highlighting differences through timelines, transcripts, and video previews. Creators have the flexibility to regenerate and refine AI suggestions as they compare alternatives. Our study participants (N=12) could easily compare and customize alternatives, creating more satisfying results.

Short-form videos are popular on platforms like TikTok and Instagram as they quickly capture viewers' attention. Many creators repurpose their long-form videos to produce short-form videos, but creators report that planning, extracting, and arranging clips from long-form videos is challenging. Currently, creators make extractive short-form videos composed of existing long-form video clips or abstractive short-form videos by adding newly recorded narration to visuals. While extractive videos maintain the original connection between audio and visuals, abstractive videos offer flexibility in selecting content to be included in a shorter time. We present Lotus, a system that combines both approaches to balance preserving the original content with flexibility over the content. Lotus first creates an abstractive short-form video by generating both a short-form script and its corresponding speech, then matching long-form video clips to the generated narration. Creators can then add extractive clips with an automated method or Lotus's editing interface. Lotus's interface can be used to further refine the short-form video. We compare short-form videos generated by Lotus with those using an extractive baseline method. In our user study, we compare creating short-form videos using Lotus to participants' existing practice.

Short videos on platforms such as TikTok, Instagram Reels, and YouTube Shorts (i.e. short-form videos) have become a primary source of information and entertainment. Many short-form videos are inaccessible to blind and low vision (BLV) viewers due to their rapid visual changes, on-screen text, and music or meme-audio overlays. In our formative study, 7 BLV viewers who regularly watched short-form videos reported frequently skipping such inaccessible content. We present ShortScribe, a system that provides hierarchical visual summaries of short-form videos at three levels of detail to support BLV viewers in selecting and understanding short-form videos. ShortScribe allows BLV users to navigate between video descriptions based on their level of interest. To evaluate ShortScribe, we assessed description accuracy and conducted a user study with 10 BLV participants comparing ShortScribe to a baseline interface. When using ShortScribe, participants reported higher comprehension and provided more accurate summaries of video content.

Blind and low vision (BLV) developers create websites to share knowledge and showcase their work. A well-designed website can engage audiences and deliver information effectively, yet it remains challenging for BLV developers to review their web designs. We conducted interviews with BLV developers (N=9) and analyzed 20 websites created by BLV developers. BLV developers created highly accessible websites but wanted to assess the usability of their websites for sighted users and follow the design standards of other websites. They also encountered challenges using screen readers to identify illegible text, misaligned elements, and inharmonious colors. We present DesignChecker, a browser extension that helps BLV developers improve their web designs. With DesignChecker, users can assess their current design by comparing it to visual design guidelines, a reference website of their choice, or a set of similar websites. DesignChecker also identifies the specific HTML elements that violate design guidelines and suggests CSS changes for improvements. Our user study participants (N=8) recognized more visual design errors than using their typical workflow and expressed enthusiasm about using DesignChecker in the future.

Sighted and blind and low vision (BLV) creators alike use videos to communicate with broad audiences. Yet, video editing remains inaccessible to BLV creators. Our formative study revealed that current video editing tools make it difficult to access the visual content, assess the visual quality, and efficiently navigate the timeline. We present AVscript, an accessible text-based video editor. AVscript enables users to edit their video using a script that embeds the video’s visual content, visual errors (e.g., dark or blurred footage), and speech. Users can also efficiently navigate between scenes and visual errors or locate objects in the frame or spoken words of interest. A comparison study (N=12) showed that AVscript significantly lowered BLV creators’ mental demands while increasing confidence and independence in video editing. We further demonstrate the potential of AVscript through an exploratory study (N=3) where BLV creators edited their own footage.

Blind and low vision (BLV) creators use images to communicate with sighted audiences. However, creating or retrieving images is challenging for BLV creators as it is difficult to use authoring tools or assess image search results. Thus, creators limit the types of images they create or recruit sighted collaborators. While text-to-image generation models let creators generate high-fidelity images based on a text description (i.e. prompt), it is difficult to assess the content and quality of generated images. We present GenAssist, a system to make text-to-image generation accessible. Using our interface, creators can verify whether generated image candidates followed the prompt, access additional details in the image not specified in the prompt, and skim a summary of similarities and differences between image candidates. To power the interface, GenAssist uses a large language model to generate visual questions, vision-language models to extract answers, and a large language model to summarize the results. Our study with 12 BLV creators demonstrated that GenAssist enables and simplifies the process of image selection and generation, making visual authoring more accessible to all.

Webtoon is a type of digital comics read online where readers can leave comments to share their thoughts on the story. While it has experienced a surge in popularity internationally, people with visual impairments cannot enjoy webtoon with the lack of an accessible format. While traditional image description practices can be adopted, resulting descriptions cannot preserve webtoons’ unique values such as control over the reading pace and social engagement through comments. To improve the webtoon reading experience for BLV users, we propose Cocomix, an interactive webtoon reader that leverages comments into the design of novel webtoon interactions. Since comments can identify story highlights and provide additional context, we designed a system that provides 1) comments-based adaptive descriptions with selective access to details and 2) panel-anchored comments for easy access to relevant descriptive comments. Our evaluation (N=12) showed that Cocomix users could adapt the description for various needs and better utilize comments.

Makeup and cosmetics offer the potential for self-expression and the reshaping of social roles for visually impaired people. However, there exist barriers to conducting a beauty regime because of the reliance on visual information and color variances in makeup. We present a content analysis of 145 YouTube videos to demonstrate visually impaired individuals’ unique practices before, during, and after doing makeup. Based on the makeup practices, we then conducted semi-structured interviews with 12 visually impaired people to discuss their perceptions of and challenges with the makeup process in more depth. Overall, through our findings and discussion, we present novel perceptions of makeup from visually impaired individuals (e.g., broader representations of blindness and beauty). The existing challenges provide opportunities for future research to address learning barriers, insufficient feedback, and physical and environmental barriers, making the experience of doing makeup more accessible to people with visual impairments.

Directly manipulating the timeline, such as scrubbing for thumbnails, is the standard way of controlling how-to videos. However, when how-to videos involve physical activities, people inconveniently alternate between controlling the video and performing the tasks. Adopting a voice user interface allows people to control the video with voice while performing the tasks with hands. However, naively translating timeline manipulation into voice user interfaces (VUI) results in temporal referencing (e.g. “rewind 20 seconds”), which requires a different mental model for navigation and thereby limiting users’ ability to peek into the content. We present RubySlippers, a system that supports efficient content-based voice navigation through keyword-based queries. Our computational pipeline automatically detects referenceable elements in the video, and finds the video segmentation that minimizes the number of needed navigational commands. Our evaluation (N=12) shows that participants could perform three representative navigation tasks with fewer commands and less frustration using RubySlippers than the conventional voice-enabled video interface.