  We introduce Motion-I2V, a novel framework for consistent and controllable
image-to-video generation (I2V). In contrast to previous methods that directly
learn the complicated image-to-video mapping, Motion-I2V factorizes I2V into
two stages with explicit motion modeling. For the first stage, we propose a
diffusion-based motion field predictor, which focuses on deducing the
trajectories of the reference image's pixels. For the second stage, we propose
motion-augmented temporal attention to enhance the limited 1-D temporal
attention in video latent diffusion models. This module can effectively
propagate reference image's feature to synthesized frames with the guidance of
predicted trajectories from the first stage. Compared with existing methods,
Motion-I2V can generate more consistent videos even at the presence of large
motion and viewpoint variation. By training a sparse trajectory ControlNet for
the first stage, Motion-I2V can support users to precisely control motion
trajectories and motion regions with sparse trajectory and region annotations.
This offers more controllability of the I2V process than solely relying on
textual instructions. Additionally, Motion-I2V's second stage naturally
supports zero-shot video-to-video translation. Both qualitative and
quantitative comparisons demonstrate the advantages of Motion-I2V over prior
approaches in consistent and controllable image-to-video generation. Please see
our project page at https://xiaoyushi97.github.io/Motion-I2V/.
