PixelHuman: Animatable Neural Radiance Fields from Few Images
Gyumin Shim Jaeseong Lee Junha Hyung Jaegul Choo
In this paper, we propose PixelHuman, a novel human rendering model that
generates animatable human scenes from a few images of a person with unseen
identity, views, and poses. Previous work have demonstrated reasonable
performance in novel view and pose synthesis, but they rely on a large number
of images to train and are trained per scene from videos, which requires
significant amount of time to produce animatable scenes from unseen human
images. Our method differs from existing methods in that it can generalize to
any input image for animatable human synthesis. Given a random pose sequence,
our method synthesizes each target scene using a neural radiance field that is
conditioned on a canonical representation and pose-aware pixel-aligned
features, both of which can be obtained through deformation fields learned in a
data-driven manner. Our experiments show that our method achieves
state-of-the-art performance in multiview and novel pose synthesis from
few-shot images.