  Creating controllable, photorealistic, and geometrically detailed digital
doubles of real humans solely from video data is a key challenge in Computer
Graphics and Vision, especially when real-time performance is required. Recent
methods attach a neural radiance field (NeRF) to an articulated structure,
e.g., a body model or a skeleton, to map points into a pose canonical space
while conditioning the NeRF on the skeletal pose. These approaches typically
parameterize the neural field with a multi-layer perceptron (MLP) leading to a
slow runtime. To address this drawback, we propose TriHuman a novel
human-tailored, deformable, and efficient tri-plane representation, which
achieves real-time performance, state-of-the-art pose-controllable geometry
synthesis as well as photorealistic rendering quality. At the core, we
non-rigidly warp global ray samples into our undeformed tri-plane texture
space, which effectively addresses the problem of global points being mapped to
the same tri-plane locations. We then show how such a tri-plane feature
representation can be conditioned on the skeletal motion to account for dynamic
appearance and geometry changes. Our results demonstrate a clear step towards
higher quality in terms of geometry and appearance modeling of humans as well
as runtime performance.
