  We present a method for multi-concept customization of pretrained
text-to-video (T2V) models. Intuitively, the multi-concept customized video can
be derived from the (non-linear) intersection of the video manifolds of the
individual concepts, which is not straightforward to find. We hypothesize that
sequential and controlled walking towards the intersection of the video
manifolds, directed by text prompting, leads to the solution. To do so, we
generate the various concepts and their corresponding interactions,
sequentially, in an autoregressive manner. Our method can generate videos of
multiple custom concepts (subjects, action and background) such as a teddy bear
running towards a brown teapot, a dog playing violin and a teddy bear swimming
in the ocean. We quantitatively evaluate our method using videoCLIP and DINO
scores, in addition to human evaluation. Videos for results presented in this
paper can be found at https://github.com/divyakraman/MultiConceptVideo2024.
