  Stylized view generation of scenes captured casually using a camera has
received much attention recently. The geometry and appearance of the scene are
typically captured as neural point sets or neural radiance fields in the
previous work. An image stylization method is used to stylize the captured
appearance by training its network jointly or iteratively with the structure
capture network. The state-of-the-art SNeRF method trains the NeRF and
stylization network in an alternating manner. These methods have high training
time and require joint optimization. In this work, we present StyleTRF, a
compact, quick-to-optimize strategy for stylized view generation using TensoRF.
The appearance part is fine-tuned using sparse stylized priors of a few views
rendered using the TensoRF representation for a few iterations. Our method thus
effectively decouples style-adaption from view capture and is much faster than
the previous methods. We show state-of-the-art results on several scenes used
for this purpose.
