Abstract: In our previous work [21], we have shown that by means of a model-based approach, relightable free-viewpoint videos of human actors can be reconstructed from only a handful of multi-view video streams recorded under calibrated illumination. To achieve this purpose, we employ a marker-free motion capture approach to measure dynamic human scene geometry. Reflectance samples for each surface point are captured by exploiting the fact that, due to the person's motion, each surface location is, over time, exposed to the acquisition sensors under varying orientations. Although this is the first setup of its kind to measure surface reflectance from footage of arbitrary human performances, our approach may lead to a biased sampling of surface reflectance since each surface point is only seen under a limited number of half-vector directions. We thus propose in this paper a novel algorithm that reduces the bias in BRDF estimates of a single surface point by cleverly taking into account reflectance samples from other surface locations made of similar material. We demonstrate the improvements achieved with this spatio-temporal reflectance sharing approach both visually and quantitatively.