This paper introduces a region-of-interest visual hull refinement technique, based on flexible voxel grids for volumetric visual hull reconstructions. Region-of-interest refinement is based on a multipass process, beginning with a focussed visual hull reconstruction, resulting in a first 3D approximation of the target, followed by a region-of-interest estimation, tasked with identifying features of interest, which in turn are used to locally refine the voxel grid and extract a higher-resolution surface representation for those regions. This approach is illustrated for the reconstruction of avatars for use in tele-immersion environments, where head and hand regions are of higher interest. To allow reproducability and direct comparison a publicly available data set for human visual hull reconstruction is used. This paper shows that region-of-interest reconstruction of the target is faster and visually comparable to higher resolution focused visual hull reconstructions. This approach red...