mlsanityData to 14 Oct 2026

AUGlasses: Continuous Action Unit based Facial Reconstruction with Low-power IMUs on Smart Glasses

arXiv 2405.13289 ↗First posted 22 May 2024cs.HC cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Recent advancements in augmented reality (AR) have enabled the use of various sensors on smart glasses for applications like facial reconstruction, which is vital to improve AR experiences for virtual social activities. However, the size and power constraints of smart glasses demand a miniature and low-power sensing solution. AUGlasses achieves unobtrusive low-power facial reconstruction by placing inertial measurement units (IMU) against the temporal area on the face to capture the skin deformations, which are caused by facial muscle movements. These IMU signals, along with historical data on facial action units (AUs), are processed by a transformer-based deep learning model to estimate AU intensities in real-time, which are then used for facial reconstruction. Our results show that AUGlasses accurately predicts the strength (0-5 scale) of 14 key AUs with a cross-user mean absolute error (MAE) of 0.187 (STD = 0.025) and achieves facial reconstruction with a cross-user MAE of 1.93 mm (STD = 0.353). We also integrated various preprocessing and training techniques to ensure robust performance for continuous sensing. Micro-benchmark tests indicate that our system consistently performs accurate continuous facial reconstruction with a fine-tuned cross-user model, achieving an AU MAE of 0.35.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 69 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

14 of 69 references.

References · 14

  1. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  2. 2016Learning Detailed Face Reconstruction from a Single Imagecited by 71
  3. 2018Style Aggregated Network for Facial Landmark Detectioncited by 60
  4. 2021Machine Translation Decoding beyond Beam Searchcited by 55
  5. 2022What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?cited by 40
  6. 2022DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Imagecited by 31
  7. 2018Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attentioncited by 27
  8. 2022Transformer-Based Self-Supervised Learning for Emotion Recognitioncited by 16
  9. 2021Confidence-Aware Scheduled Sampling for Neural Machine Translationcited by 10
  10. 2019Face Alignment using a 3D Deeply-initialized Ensemble of Regression Treescited by 8
  11. 2023InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognitioncited by 5
  12. 2021WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensingcited by 4
2 more
  1. 2024Fiducial Focus Augmentation for Facial Landmark Detectioncited by 2
  2. 2017Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environmentcited by 1

Built on by

Not cited yet.