  The advent of deep learning has led to significant progress in monocular
human reconstruction. However, existing representations, such as parametric
models, voxel grids, meshes and implicit neural representations, have
difficulties achieving high-quality results and real-time speed at the same
time. In this paper, we propose Fourier Occupancy Field (FOF), a novel
powerful, efficient and flexible 3D representation, for monocular real-time and
accurate human reconstruction. The FOF represents a 3D object with a 2D field
orthogonal to the view direction where at each 2D position the occupancy field
of the object along the view direction is compactly represented with the first
few terms of Fourier series, which retains the topology and neighborhood
relation in the 2D domain. A FOF can be stored as a multi-channel image, which
is compatible with 2D convolutional neural networks and can bridge the gap
between 3D geometries and 2D images. The FOF is very flexible and extensible,
e.g., parametric models can be easily integrated into a FOF as a prior to
generate more robust results. Based on FOF, we design the first 30+FPS
high-fidelity real-time monocular human reconstruction framework. We
demonstrate the potential of FOF on both public dataset and real captured data.
The code will be released for research purposes.
