Faces as Lighting Probes via Unsupervised Deep Highlight Extraction

@inproceedings{Yi2018FacesAL,
  title={Faces as Lighting Probes via Unsupervised Deep Highlight Extraction},
  author={Renjiao Yi and Chenyang Zhu and Ping Tan and Stephen Lin},
  booktitle={ECCV},
  year={2018}
}
We present a method for estimating detailed scene illumination using human faces in a single image. [] Key Method Based on the observation that faces can exhibit strong highlight reflections from a broad range of lighting directions, we propose a deep neural network for extracting highlights from faces, and then trace these reflections back to the scene to acquire the environment map.
Hybrid Face Reflectance, Illumination, and Shape from a Single Image.
TLDR
This work proposes HyFRIS-Net to jointly estimate the hybrid reflectance and illumination models, as well as the refined face shape from a single unconstrained face image in a pre-defined texture space to ensure photometric face appearance modeling in both parametric and non-parametric spaces for efficient learning.
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This work adopts the structure of conditional generative adversarial network (CGAN) to generate highlight-free images in facial images, which is, to the best knowledge, the largest image dataset for facial highlight removal.
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It is shown that the predicted HDR environment maps can be used as accurate illumination sources for scene renderings, with potential applications in 3D object insertion for augmented reality.
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This contribution aims to bring together in a coherent manner current advances in this conjunction, presented in three categories: scene illumination estimation, relighting with reflectance‐aware scene‐specific representations and finally relighting as image‐to‐image transformations.
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An approach that takes advantage of physical principles from inverse rendering to constrain the solution, while also utilizing neural networks to expedite the more computationally expensive portions of its processing, to increase robustness to noisy input data as well as to improve temporal and spatial stability is proposed.
Learning Illumination from Diverse Portraits
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This work presents a learning-based technique for estimating high dynamic range, omnidirectional illumination from a single low dynamic range portrait image captured under arbitrary indoor or outdoor lighting conditions, and shows that this technique outperforms the state-of-the-art technique for portrait-based lighting estimation.
Learning Scene Illumination by Pairwise Photos from Rear and Front Mobile Cameras
TLDR
A learning based method to recover low‐frequency scene illumination represented as spherical harmonic functions by pairwise photos from rear and front cameras on mobile devices is proposed and produces visually and quantitatively superior results compared to the state‐of‐the‐arts.
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