  In this paper, we present TOSS, which introduces text to the task of novel
view synthesis (NVS) from just a single RGB image. While Zero-1-to-3 has
demonstrated impressive zero-shot open-set NVS capability, it treats NVS as a
pure image-to-image translation problem. This approach suffers from the
challengingly under-constrained nature of single-view NVS: the process lacks
means of explicit user control and often results in implausible NVS
generations. To address this limitation, TOSS uses text as high-level semantic
information to constrain the NVS solution space. TOSS fine-tunes text-to-image
Stable Diffusion pre-trained on large-scale text-image pairs and introduces
modules specifically tailored to image and camera pose conditioning, as well as
dedicated training for pose correctness and preservation of fine details.
Comprehensive experiments are conducted with results showing that our proposed
TOSS outperforms Zero-1-to-3 with more plausible, controllable and
multiview-consistent NVS results. We further support these results with
comprehensive ablations that underscore the effectiveness and potential of the
introduced semantic guidance and architecture design.
