  The sound effects that designers add to videos are designed to convey a
particular artistic effect and, thus, may be quite different from a scene's
true sound. Inspired by the challenges of creating a soundtrack for a video
that differs from its true sound, but that nonetheless matches the actions
occurring on screen, we propose the problem of conditional Foley. We present
the following contributions to address this problem. First, we propose a
pretext task for training our model to predict sound for an input video clip
using a conditional audio-visual clip sampled from another time within the same
source video. Second, we propose a model for generating a soundtrack for a
silent input video, given a user-supplied example that specifies what the video
should "sound like". We show through human studies and automated evaluation
metrics that our model successfully generates sound from video, while varying
its output according to the content of a supplied example. Project site:
https://xypb.github.io/CondFoleyGen/
