  Generating video background that tailors to foreground subject motion is an
important problem for the movie industry and visual effects community. This
task involves synthesizing background that aligns with the motion and
appearance of the foreground subject, while also complies with the artist's
creative intention. We introduce ActAnywhere, a generative model that automates
this process which traditionally requires tedious manual efforts. Our model
leverages the power of large-scale video diffusion models, and is specifically
tailored for this task. ActAnywhere takes a sequence of foreground subject
segmentation as input and an image that describes the desired scene as
condition, to produce a coherent video with realistic foreground-background
interactions while adhering to the condition frame. We train our model on a
large-scale dataset of human-scene interaction videos. Extensive evaluations
demonstrate the superior performance of our model, significantly outperforming
baselines. Moreover, we show that ActAnywhere generalizes to diverse
out-of-distribution samples, including non-human subjects. Please visit our
project webpage at https://actanywhere.github.io.
