  Temporal consistency is essential for video editing applications. Existing
work on layered representation of videos allows propagating edits consistently
to each frame. These methods, however, can only edit object appearance rather
than object shape changes due to the limitation of using a fixed UV mapping
field for texture atlas. We present a shape-aware, text-driven video editing
method to tackle this challenge. To handle shape changes in video editing, we
first propagate the deformation field between the input and edited keyframe to
all frames. We then leverage a pre-trained text-conditioned diffusion model as
guidance for refining shape distortion and completing unseen regions. The
experimental results demonstrate that our method can achieve shape-aware
consistent video editing and compare favorably with the state-of-the-art.

Computer Vision and Pattern Recognition;Image and Video Processing