Pradyumna Ym

The Deaf or Hard-of-Hearing (DHH) community constitutes over 430 million people globally, with about 70 million of them using sign language as their primary means of communication. Learning sign language poses significant challenges, and researchers have explored the potential of digital games as educational tools for teaching sign language. However, existing educational games are limited to a narrow range of words and feature only single-player modes. Consequently, our objective was to design a sign language-based game that not only facilitates learning but also enables social gameplay and encourages user-generated content. In this work, we present SignIt!, an accessible quiz platform co-designed with the DHH community. SignIt! empowers DHH players to play sign language-based quizzes either individually or in competitive settings, as well as to create their own quiz content. Through a user study involving five DHH participants, we found SignIt! was deemed easy-to-learn, intuitive, and accessible by our participants. They expressed various motivations for using SignIt!, including passing time, learning quiz content, and connecting with friends.

The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard unfamiliar out-of-distribution data. To this end, we cast the 3D human pose learning as an unsupervised domain adaptation problem. We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations; a) model-free joint localization and b) model-based parametric regression. Such a design allows us to derive suitable measures to quantify prediction uncertainty at both pose and joint level granularity. While supervising only on labeled synthetic samples, the adaptation process aims to minimize the uncertainty for the unlabeled target images while maximizing the same for an extreme out-of-distribution dataset (backgrounds). Alongside synthetic-to-real 3D pose adaptation, the joint-uncertainties allow expanding the adaptation to work on in-the-wild images even in the presence of occlusion and truncation scenarios. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.