TY - GEN
T1 - Eye-See-You
T2 - 34th Internationa Joint Conference on Artificial Intelligence, IJCAI 2025
AU - Dash, Ankan
AU - Gu, Jingyi
AU - Wang, Guiling
AU - Chen, Chen
N1 - Publisher Copyright:
© 2025 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105021816835
UR - https://www.scopus.com/pages/publications/105021816835#tab=citedBy
U2 - 10.24963/ijcai.2025/1020
DO - 10.24963/ijcai.2025/1020
M3 - Conference contribution
AN - SCOPUS:105021816835
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 9176
EP - 9184
BT - Proceedings of the 34th International Joint Conference on Artificial Intelligence, IJCAI 2025
A2 - Kwok, James
PB - International Joint Conferences on Artificial Intelligence
Y2 - 16 August 2025 through 22 August 2025
ER -