mlsanityData to 14 Oct 2026

Facial De-occlusion Network for Virtual Telepresence Systems

arXiv 2210.12622 ↗First posted 23 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

To see what is not in the image is one of the broader missions of computer vision. Technology to inpaint images has made significant progress with the coming of deep learning. This paper proposes a method to tackle occlusion specific to human faces. Virtual presence is a promising direction in communication and recreation for the future. However, Virtual Reality (VR) headsets occlude a significant portion of the face, hindering the photo-realistic appearance of the face in the virtual world. State-of-the-art image inpainting methods for de-occluding the eye region does not give usable results. To this end, we propose a working solution that gives usable results to tackle this problem enabling the use of the real-time photo-realistic de-occluded face of the user in VR settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 23 Oct 2022.

Builds on

11 of 17 references.

References · 11

  1. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  2. 2020First Order Motion Model for Image Animationcited by 704
  3. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  4. 2018Generative Image Inpainting with Contextual Attentioncited by 580
  5. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
  6. 2020One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencingcited by 444
  7. 2018Deep Appearance Models for Face Renderingcited by 164
  8. 2019EdgeConnect: Generative Image Inpainting with Adversarial Edge Learningcited by 163
  9. 2019LaFIn: Generative Landmark Guided Face Inpaintingcited by 12
  10. 2021Egocentric Videoconferencingcited by 10
  11. 2021Attention based Occlusion Removal for Hybrid Telepresence Systemscited by 2

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