Abstract
Face possesses a rich spatial structure that can provide valuable cues to guide various face-related tasks. The eyes are considered an important socio-visual cue for effective communication. They are an integral feature of facial expressions as they are an important aspect of interpersonal communication. However, virtual reality headsets occlude a significant portion of the face and restrict the visibility of certain facial features, particularly the eye region. Reproducing this region with realistic content and handling complex eye movements such as blinks is challenging. Previous facial inpainting methods are not capable enough to capture subtle eye movements. In view of this, we propose a working solution to refine the reconstructions, particularly around the eye region, by leveraging inherent eye structure. We introduce spatial supervision and a novel landmark predictor module to regularize per-frame reconstructions obtained from an existing image-based facial de-occlusion network. Experiments verify the usefulness of our approach in enhancing the quality of reconstructions to capture subtle eye movements.
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Gupta, S., Jinka, S.S., Sharma, A., Namboodiri, A. (2023). Supervision by Landmarks: An Enhanced Facial De-occlusion Network for VR-Based Applications. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13805. Springer, Cham. https://doi.org/10.1007/978-3-031-25072-9_21
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DOI: https://doi.org/10.1007/978-3-031-25072-9_21
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