  For reconstructing high-fidelity human 3D models from monocular videos, it is
crucial to maintain consistent large-scale body shapes along with finely
matched subtle wrinkles. This paper explores the observation that the per-frame
rendering results can be factorized into a pose-independent component and a
corresponding pose-dependent equivalent to facilitate frame consistency. Pose
adaptive textures can be further improved by restricting frequency bands of
these two components. In detail, pose-independent outputs are expected to be
low-frequency, while highfrequency information is linked to pose-dependent
factors. We achieve a coherent preservation of both coarse body contours across
the entire input video and finegrained texture features that are time variant
with a dual-branch network with distinct frequency components. The first branch
takes coordinates in canonical space as input, while the second branch
additionally considers features outputted by the first branch and pose
information of each frame. Our network integrates the information predicted by
both branches and utilizes volume rendering to generate photo-realistic 3D
human images. Through experiments, we demonstrate that our network surpasses
the neural radiance fields (NeRF) based state-of-the-art methods in preserving
high-frequency details and ensuring consistent body contours.
