We present a novel single-shot method for capturing high-quality facial appearance that enables per-pixel estimation of diffuse albedo, specular albedo, specular roughness, and photometric (specular) normals using only linearly polarized RGB illumination and consumer cameras. Our approach leverages color-multiplexed sinusoidal lighting encoded across RGB channels, allowing phase-based decomposition of reflectance parameters per view, without time-multiplexing or iterative refinement. Unlike prior works that jointly optimize appearance and geometry, we decouple the estimation process – separately recovering spatially-varying specular albedo and roughness for the first time in a single-shot capture setup. Additionally, we estimate high-frequency specular photometric normals independently using view-dependent specular phase cues, obtaining accurate surface mesostructure. We demonstrate our system using a practical monitor-based capture setup with 15 polarized DSLRs, producing detailed reflectance maps suitable for photorealistic rendering. Our approach achieves higher accuracy in reflectance separation and more accurate estimation of fine-scale surface details compared to previous single-shot methods.

We propose a practical method to measure spectral skin reflectance as well as a spectral BSSRDF model spanning a wide spectral range from 300nm to 1000nm. We employ a practical capture setup consisting of desktop monitors to illuminate human faces in the visible domain to estimate five parameters of spectral chromophore concentrations including melanin, hemoglobin, and $\beta$ carotene concentration, melanin blend-type fraction, and epidermal hemoglobin fraction. The estimated parameters make use of a novel three-stage lookup table search for faster parameter fitting, and drive our skin model for accurate reconstruction of facial skin reflectance response in both the visible domain as well as in the UVA and near-infrared range. We also propose a novel neural network architecture that given our measurements, predicts the five chromophore parameters of our model at the encoder stage and full hyperspectral reflectance response as the output of the decoder stage.

Abstract While current facial re‐ageing methods can produce realistic results, they purely focus on the 2D age transformation. In this work, we present an approach to transform the age of a person in both facial appearance and shape across different ages while preserving their identity. We employ an α‐(de)blending diffusion network with an age‐to‐α transformation to generate coarse structure changes, such as wrinkles. Additionally, we edit biophysical skin properties, including melanin and hemoglobin, to simulate skin color changes, producing realistic re‐ageing results from ages 10 to 80 years. We also propose a geometric neural network that alters the coarse scale facial geometry according to age, followed by a lightweight and efficient network that adds appropriate skin displacement on top of the coarse geometry. Both qualitative and quantitative comparisons show that our method outperforms current state‐of‐the‐art approaches.

Abstract We present a practical method for acquisition of shape and plausible appearance of reflective and translucent objects for realistic rendering and relighting applications. Such objects are extremely challenging to scan with existing capture setups, and have previously required complex lightstage hardware emitting continuous illumination. We instead employ a practical capture setup consisting of a set of desktop LCD screens to illuminate such objects with piece‐wise continuous illumination for acquisition. We employ phase‐shifted sinusoidal illumination for novel estimation of high quality photometric normals and transmission vector along with diffuse‐specular separated reflectance/transmission maps for realistic relighting. We further employ neural in‐painting to fill gaps in our measurements caused by gaps in screen illumination, and a novel NeuS‐based neural rendering that combines these shape and reflectance maps acquired from multiple viewpoints for high‐quality 3D surface geometry reconstruction along with plausible realistic rendering of complex light transport in such objects.