VR is often utilized for organizing virtual events such as meetings, conferences, and concerts; however, support for live production is lacking in most existing VR tools.We present XCam, a toolkit enabling mixed-initiative control over virtual camera systems-from fully manual control by users to increasingly automated, systemdriven control with minimal user intervention.XCam's architectural design separates the concerns of object tracking, camera motion, and scene transition, giving more degrees of freedom to operators who can adjust the level of automation along all three dimensions.We used XCam to conduct two studies: (1) interviews with six VR content creators probe into what aspects should and shouldn't be automated based on six applications developed with XCam; (2) three workshops with experts explore XCam's utility in live production of an interactive VR flm sequence, a lecture on cinematography, and an alumni meeting in social VR.Expert feedback from our studies suggests how to balance automation and control, and the opportunities and limits of future AI-driven tools.

Numerous digital reminder solutions have been created on smartphones, tablets, and smart home devices, to support people with dementia. Yet, these digital reminder systems often overlook the importance of non-digital memory aids, such as paper calendars, diaries, and household objects, in the lives of people with dementia. Given recent work suggests that with progression of dementia, users are likely to minimize use of digital devices, accounting for non-digital practices around remembering presents an opportunity that can be leveraged when designing reminder technologies for people with dementia. This poster proposes a voice-based reminder system that connects both physical and digital reminders through embodied home objects and can be customized to the unique routines of the individual with dementia. We present the system architecture and usage scenarios that we implemented for feedback and iteration in future participatory design studies.

Recent technological advances are enabling HCI researchers to explore interaction possibilities for remote XR collaboration using high-fidelity reconstructions of physical activity spaces. However, creating these reconstructions often lacks user involvement with an overt focus on capturing sensory context that does not necessarily augment an informal social experience. This work seeks to understand social context that can be important for reconstruction to enable XR applications for informal instructional scenarios. Our study involved the evaluation of an XR remote guidance prototype by 8 intergenerational groups of closely related gardeners using reconstructions of personally meaningful spaces in their gardens. Our findings contextualize physical objects and areas with various motivations related to gardening and detail perceptions of XR that might affect the use of reconstructions for remote interaction. We discuss implications for user involvement to create reconstructions that better translate real-world experience, encourage reflection, incorporate privacy considerations, and preserve shared experiences with XR as a medium for informal intergenerational activities.

Intergenerational social interactions are beneficial for bridging generational gaps, strengthening family bonds, and improving social cohesiveness. However, opportunities for in-person intergenerational social interactions are decreasing as families become increasingly geographically dispersed. Researchers are examining how technology might support these interactions. Extended Reality (XR) is an emerging technology that has shown potential for supporting immersive remote interactions but might cause an "experience asymmetry" in an intergenerational setting. In this poster we contrast the user experience of younger and older participants engaging in remote gardening sessions with our social XR prototypes. We present systemic influence factors that affected user experience of participants from different age groups differently with our XR prototypes. We discuss potential approaches to mitigate their effects based on observational learning and becoming aware of designer biases.

As extended reality (XR) systems become increasingly available, XR-based remote instruction is being adopted for diverse purposes in professional settings such as surgery and field servicing. Hobbyists have been well-studied in HCI and may similarly benefit from remote skill-sharing. However, little is known about how XR technologies might support expert-novice collaboration for skilled hobby activities. This paper examines the potential and limitations of XR to connect experts and novices for one such activity: gardening. Through two studies involving 27 expert and novice gardeners, we designed prototypes to understand 1) practitioner perceptions of XR and remote skill-sharing in the garden and 2) what kinds of interactions can be supported in XR for expert-novice groups. We discuss design opportunities and challenges for XR systems in supporting informal connecting interactions and meaningful sensory interactions with a remote environment during skill-sharing.

Gardening is an activity that involves a number of dimensions of increasing interest to HCI and CSCW researchers, including recreation, sustainability, and engagement with nature. This paper considers the garden setting in order to understand the role that collaborative and social computing technologies might play for practitioners engaging in outdoor skilled activities. We conducted participant observations with nine experienced gardeners aged 22-71 years. Through this process, we find that gardeners continuously configure their environments to accommodate their preferences for sociality. They share embodied skills and help others attune to sensory information in person, but also influence learning through the features in their garden that are observed by others. This paper provides an understanding of sociality in the garden, highlights skill sharing as a key domain for design in this space, and contributes design considerations for collaborative technologies in outdoor settings.

We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement these findings, we report on an interview study with three key stakeholder groups (N=14) soliciting reactions to our tool and methods. Our findings demonstrate the potential of virtually auditing urban accessibility and highlight tradeoffs between scalability and quality compared to traditional approaches.

We present Project Sidewalk, a new online tool that allows anyone-from motivated citizens to government workers-to remotely label accessibility problems by virtually walking through city streets. Basic game design principles such as interactive onboarding, mission-based tasks, and stats dashboards are used to train, engage, and sustain users. We describe the current Project Sidewalk system, present results of a pilot public deployment with 581 users, and discuss open questions and future work.

Inferring the social structures of animal groups from their observed behavior is a non-trivial task usually handled by direct observation. Recent advances in sensing and tracking technology have enabled the collection of dense spatial data over long periods of time automatically. The qualitative differences between sparse hand-coded data and dense tracking data necessitate a new approach to inferring the social structure of the observed animals. We present a framework for using agent-based simulations to guide our approach to inferring social structure from tracking data collected from a small group of rhesus macaques over a period of three months. As part of this framework, we describe a version of the DOMWORLD model of dominance interactions in rhesus macaques that has been modified to include association preference, and adapted to more closely match the environment where the monkeys were housed. An exploration of simulation results reveals important characteristics of the tracking data. The inferred social structures of the tracked monkeys are also presented.