A delighting network trained via Dataset Latent Modulation on heterogeneous OLAT and Light Stage data enables high-quality in-the-wild facial reflectance capture from video and produces the NeRSemble-Scan dataset.
arXiv:2206.03380 (2022) 4
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AEGIR introduces explicit area-emitter modeling inside a relightable Gaussian Splatting pipeline together with a differentiable deferred renderer using multiple importance sampling and regularization to improve lighting-material decomposition.
GaNI combines NeuS geometry reconstruction with a light-position-aware inverse neural radiosity stage that adds implicit near-field modeling, surface angle loss, and roughness smoothness priors to recover reflectance parameters from co-located light-camera captures.
citing papers explorer
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Learning a Delighting Prior for Facial Appearance Capture in the Wild
A delighting network trained via Dataset Latent Modulation on heterogeneous OLAT and Light Stage data enables high-quality in-the-wild facial reflectance capture from video and produces the NeRSemble-Scan dataset.
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AEGIR: Modeling Area Emitters for Indoor Inverse Rendering using Gaussian Splatting
AEGIR introduces explicit area-emitter modeling inside a relightable Gaussian Splatting pipeline together with a differentiable deferred renderer using multiple importance sampling and regularization to improve lighting-material decomposition.
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GaNI: Global and Near Field Illumination Aware Neural Inverse Rendering
GaNI combines NeuS geometry reconstruction with a light-position-aware inverse neural radiosity stage that adds implicit near-field modeling, surface angle loss, and roughness smoothness priors to recover reflectance parameters from co-located light-camera captures.