  Text-to-video generation has shown promising results. However, by taking only
natural languages as input, users often face difficulties in providing detailed
information to precisely control the model's output. In this work, we propose
fine-grained controllable video generation (FACTOR) to achieve detailed
control. Specifically, FACTOR aims to control objects' appearances and context,
including their location and category, in conjunction with the text prompt. To
achieve detailed control, we propose a unified framework to jointly inject
control signals into the existing text-to-video model. Our model consists of a
joint encoder and adaptive cross-attention layers. By optimizing the encoder
and the inserted layer, we adapt the model to generate videos that are aligned
with both text prompts and fine-grained control. Compared to existing methods
relying on dense control signals such as edge maps, we provide a more intuitive
and user-friendly interface to allow object-level fine-grained control. Our
method achieves controllability of object appearances without finetuning, which
reduces the per-subject optimization efforts for the users. Extensive
experiments on standard benchmark datasets and user-provided inputs validate
that our model obtains a 70% improvement in controllability metrics over
competitive baselines.
