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HeadsetOff: Enabling Photorealistic Video Conferencing on Economical VR Headsets

Yili Jin, Xize Duan, Fangxin Wang, Xue Liu

TL;DR

Experimental results demonstrate the effectiveness of HeadsetOff in achieving high-quality, low-latency video conferencing on economical VR headsets.

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.

HeadsetOff: Enabling Photorealistic Video Conferencing on Economical VR Headsets

TL;DR

Experimental results demonstrate the effectiveness of HeadsetOff in achieving high-quality, low-latency video conferencing on economical VR headsets.

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.
Paper Structure (24 sections, 4 equations, 3 figures, 4 tables, 1 algorithm)

This paper contains 24 sections, 4 equations, 3 figures, 4 tables, 1 algorithm.

Figures (3)

  • Figure 1: Overview of HeadsetOff.
  • Figure 2: Demo of HeadsetOff.
  • Figure 3: Overview of Generator.