HeadsetOff: Enabling Photorealistic Video Conferencing on Economical VR Headsets

Virtual Reality (VR) has become increasingly popular for remote collaboration, but video conferencing poses challenges when the user's face is covered by the headset. Existing solutions have limitations in terms of accessibility. In this paper, we propose HeadsetOff, a novel system that achieves photorealistic video conferencing on economical VR headsets by leveraging voice-driven face reconstruction. HeadsetOff consists of three main components: a multimodal predictor, a generator, and an adaptive controller. The predictor effectively predicts user future behavior based on different modalities. The generator employs voice, head motion, and eye blink to animate the human face. The adaptive controller dynamically selects the appropriate generator model based on the trade-off between video quality and delay. Experimental results demonstrate the effectiveness of HeadsetOff in achieving high-quality, low-latency video conferencing on economical VR headsets.

Authors

Institutions

Publication Details

Published
2024-10-26
DOI
https://doi.org/10.1145/3664647.3681432
Citations
5
Primary Topic
Virtual Reality Applications and Impacts
Type
article
Field-Weighted Citation Impact
1.86
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

HeadsetOff: Enabling Photorealistic Video Conferencing on Economical VR Headsets

Yili Jin, Xue Liu, Fangxin Wang, Xize Duan
5 citations
Virtual Reality Applications and Impacts
1.86
article

HeadsetOff: Enabling Photorealistic Video Conferencing on Economical VR Headsets

Yili Jin, Xue Liu, Fangxin Wang, Xize Duan
article en
5 citations

Abstract

Virtual Reality (VR) has become increasingly popular for remote collaboration, but video conferencing poses challenges when the user's face is covered by the headset. Existing solutions have limitations in terms of accessibility. In this paper, we propose HeadsetOff, a novel system that achieves photorealistic video conferencing on economical VR headsets by leveraging voice-driven face reconstruction. HeadsetOff consists of three main components: a multimodal predictor, a generator, and an adaptive controller. The predictor effectively predicts user future behavior based on different modalities. The generator employs voice, head motion, and eye blink to animate the human face. The adaptive controller dynamically selects the appropriate generator model based on the trade-off between video quality and delay. Experimental results demonstrate the effectiveness of HeadsetOff in achieving high-quality, low-latency video conferencing on economical VR headsets.

Chinese University of Hong Kong, Shenzhen (CN), McGill University (CA)
Openalex Percentile: Top 14%
Virtual Reality Applications and Impacts
1.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.