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

Super-Resolution Appearance Transfer for 4D Human Performances