Jingyi Gu

Ankan Dash, Jingyi Gu, Guiling Wang et al.

Machine learning techniques have shown remarkable accuracy in localization tasks, but their dependency on vast amounts of labeled data, particularly Channel State Information (CSI) and corresponding coordinates, remains a bottleneck. Self-supervised learning techniques alleviate the need for labeled data, a potential that remains largely untapped and underexplored in existing research. Addressing this gap, we propose a pioneering approach that leverages self-supervised pretraining on unlabeled data to boost the performance of supervised learning for user localization based on CSI. We introduce two pretraining Auto Encoder (AE) models employing Multi Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs) to glean representations from unlabeled data via self-supervised learning. Following this, we utilize the encoder portion of the AE models to extract relevant features from labeled data, and finetune an MLP-based Position Estimation Model to accurately deduce user locations. Our experimentation on the CTW-2020 dataset, which features a substantial volume of unlabeled data but limited labeled samples, demonstrates the viability of our approach. Notably, the dataset covers a vast area spanning over 646x943x41 meters, and our approach demonstrates promising results even for such expansive localization tasks.

Ankan Dash, Jingyi Gu, Guiling Wang et al.

Virtual Reality (VR) headsets, while integral to the evolving digital ecosystem, present a critical challenge: the occlusion of users' eyes and portions of their faces, which hinders visual communication and may contribute to social isolation. To address this, we introduce RevAvatar, an innovative framework that leverages AI methodologies to enable reverse pass-through technology, fundamentally transforming VR headset design and interaction paradigms. RevAvatar integrates state-of-the-art generative models and multimodal AI techniques to reconstruct high-fidelity 2D facial images and generate accurate 3D head avatars from partially observed eye and lower-face regions. This framework represents a significant advancement in AI4Tech by enabling seamless interaction between virtual and physical environments, fostering immersive experiences such as VR meetings and social engagements. Additionally, we present VR-Face, a novel dataset comprising 200,000 samples designed to emulate diverse VR-specific conditions, including occlusions, lighting variations, and distortions. By addressing fundamental limitations in current VR systems, RevAvatar exemplifies the transformative synergy between AI and next-generation technologies, offering a robust platform for enhancing human connection and interaction in virtual environments.