Subramanian "Subbu" Chidambaram

Computer Vision (CV) algorithms require large annotated datasets that are often labor-intensive and expensive to create. We propose AnnotateXR, an extended reality (XR) workflow to collect various high fidelity data and auto-annotate it in a single demonstration. AnnotateXR allows users to align virtual models over physical objects, tracked with 6DoF sensors. AnnotateXR utilizes a hand tracking capable XR HMD coupled with 6DoF information and collision detection to enable algorithmic segmentation of different actions in videos through its digital twin. The virtual-physical mapping provides a tight bounding volume to generate semantic segmentation masks for the captured image data. Alongside supporting object and action segmentation, we also support other dimensions of annotation required by modern CV, such as Human-Object, Object-Object, and rich 3D recordings, all with a single demonstration. Our user study shows AnnotateXR produced over 112,000 annotated data points in 67 minutes.

Augmented/Virtual reality and video-based media play a vital role in the digital learning revolution to train novices in spatial tasks. However, creating content for these different media requires expertise in several fields. We present EditAR, a unified authoring, and editing environment to create content for AR, VR, and video based on a single demonstration. EditAR captures the user’s interaction within an environment and creates a digital twin, enabling users without programming backgrounds to develop content. We conducted formative interviews with both subject and media experts to design the system. The prototype was developed and reviewed by experts. We also performed a user study comparing traditional video creation with 2D video creation from 3D recordings, via a 3D editor, which uses freehand interaction for in-headset editing. Users took 5 times less time to record instructions and preferred EditAR, along with giving significantly higher usability scores.

Augmented reality (AR) is an efficient form of delivering spatial information and has great potential for training workers. However, AR is still not widely used for such scenarios due to the technical skills and expertise required to create interactive AR content. We developed ProcessAR, an AR-based system to develop 2D/3D content that captures subject matter expert’s (SMEs) environment-object interactions in situ. The design space for ProcessAR was identified from formative interviews with AR programming experts and SMEs, alongside a comparative design study with SMEs and novice users. To enable smooth workflows, ProcessAR locates and identifies different tools/objects through computer vision within the workspace when the author looks at them. We explored additional features such as embedding 2D videos with detected objects and user-adaptive triggers. A final user evaluation comparing ProcessAR and a baseline AR authoring environment showed that, according to our qualitative questionnaire, users preferred ProcessAR.