  Video generation has witnessed great success recently, but their application
in generating long videos still remains challenging due to the difficulty in
maintaining the temporal consistency of generated videos and the high memory
cost during generation. To tackle the problems, in this paper, we propose a
brave and new idea of Multi-sentence Video Grounding for Long Video Generation,
connecting the massive video moment retrieval to the video generation task for
the first time, providing a new paradigm for long video generation. The method
of our work can be summarized as three steps: (i) We design sequential scene
text prompts as the queries for video grounding, utilizing the massive video
moment retrieval to search for video moment segments that meet the text
requirements in the video database. (ii) Based on the source frames of
retrieved video moment segments, we adopt video editing methods to create new
video content while preserving the temporal consistency of the retrieved video.
Since the editing can be conducted segment by segment, and even frame by frame,
it largely reduces the memory cost. (iii) We also attempt video morphing and
personalized generation methods to improve the subject consistency of long
video generation, providing ablation experimental results for the subtasks of
long video generation. Our approach seamlessly extends the development in
image/video editing, video morphing and personalized generation, and video
grounding to the long video generation, offering effective solutions for
generating long videos at low memory cost.
