  In this work, we present Vlogger, a generic AI system for generating a
minute-level video blog (i.e., vlog) of user descriptions. Different from short
videos with a few seconds, vlog often contains a complex storyline with
diversified scenes, which is challenging for most existing video generation
approaches. To break through this bottleneck, our Vlogger smartly leverages
Large Language Model (LLM) as Director and decomposes a long video generation
task of vlog into four key stages, where we invoke various foundation models to
play the critical roles of vlog professionals, including (1) Script, (2) Actor,
(3) ShowMaker, and (4) Voicer. With such a design of mimicking human beings,
our Vlogger can generate vlogs through explainable cooperation of top-down
planning and bottom-up shooting. Moreover, we introduce a novel video diffusion
model, ShowMaker, which serves as a videographer in our Vlogger for generating
the video snippet of each shooting scene. By incorporating Script and Actor
attentively as textual and visual prompts, it can effectively enhance
spatial-temporal coherence in the snippet. Besides, we design a concise mixed
training paradigm for ShowMaker, boosting its capacity for both T2V generation
and prediction. Finally, the extensive experiments show that our method
achieves state-of-the-art performance on zero-shot T2V generation and
prediction tasks. More importantly, Vlogger can generate over 5-minute vlogs
from open-world descriptions, without loss of video coherence on script and
actor. The code and model is all available at
https://github.com/zhuangshaobin/Vlogger.
