  A common problem in the 4D reconstruction of people from multi-view video is
the quality of the captured dynamic texture appearance which depends on both
the camera resolution and capture volume. Typically the requirement to frame
cameras to capture the volume of a dynamic performance ($>50m^3$) results in
the person occupying only a small proportion $<$ 10% of the field of view. Even
with ultra high-definition 4k video acquisition this results in sampling the
person at less-than standard definition 0.5k video resolution resulting in
low-quality rendering. In this paper we propose a solution to this problem
through super-resolution appearance transfer from a static high-resolution
appearance capture rig using digital stills cameras ($> 8k$) to capture the
person in a small volume ($<8m^3$). A pipeline is proposed for super-resolution
appearance transfer from high-resolution static capture to dynamic video
performance capture to produce super-resolution dynamic textures. This
addresses two key problems: colour mapping between different camera systems;
and dynamic texture map super-resolution using a learnt model. Comparative
evaluation demonstrates a significant qualitative and quantitative improvement
in rendering the 4D performance capture with super-resolution dynamic texture
appearance. The proposed approach reproduces the high-resolution detail of the
static capture whilst maintaining the appearance dynamics of the captured
video.
