ReVideo: Remake a Video with Motion and Content Control
Authors: name: Chong Mou, name: Mingdeng Cao, name: Xintao Wang, name: Zhaoyang Zhang, name: Ying Shan, name: Jian Zhang

Despite significant advancements in video generation and editing using
diffusion models, achieving accurate and localized video editing remains a
substantial challenge. Additionally, most existing video editing methods
primarily focus on altering visual content, with limited research dedicated to
motion editing. In this paper, we present a novel attempt to Remake a Video
(ReVideo) which stands out from existing methods by allowing precise video
editing in specific areas through the specification of both content and motion.
Content editing is facilitated by modifying the first frame, while the
trajectory-based motion control offers an intuitive user interaction
experience. ReVideo addresses a new task involving the coupling and training
imbalance between content and motion control. To tackle this, we develop a
three-stage training strategy that progressively decouples these two aspects
from coarse to fine. Furthermore, we propose a spatiotemporal adaptive fusion
module to integrate content and motion control across various sampling steps
and spatial locations. Extensive experiments demonstrate that our ReVideo has
promising performance on several accurate video editing applications, i.e., (1)
locally changing video content while keeping the motion constant, (2) keeping
content unchanged and customizing new motion trajectories, (3) modifying both
content and motion trajectories. Our method can also seamlessly extend these
applications to multi-area editing without specific training, demonstrating its
flexibility and robustness.