Video Stylization To perform video stylization, we fol-
low an approach inspired by [15, 22, 80] to predict videos
from the combination of text, optical ﬂow, and depth sig-
nals. On a subset of steps, we also condition on the ﬁrst
video frame. As described in [22], the text will generally
deﬁne the “content” or appearance of the output and the op-
tical ﬂow and depth control the “structure.” In contrast to the
diffusion-based approaches that usually use external cross-
attention networks [80] or latent blending [40] for styliza-
tion, our approach is more closely related to machine trans-
lation using large language models in that we only need
to provide the structure and text as a preﬁx to a language
model.
VideoPoet