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Text-to-video generation has shown promising results. However, by taking onlynatural languages as input, users often face difficulties in providing detailedinformation to precisely control the model's output. In this work, we proposefine-grained controllable video generation (FACTOR) to achieve detailedcontrol. Specifically, FACTOR aims to control objects' appearances and context,including their location and category, in conjunction with the text prompt. Toachieve detailed control, we propose a unified framework to jointly injectcontrol signals into the existing text-to-video model. Our model consists of ajoint encoder and adaptive cross-attention layers. By optimizing the encoderand the inserted layer, we adapt the model to generate videos that are alignedwith both text prompts and fine-grained control. Compared to existing methodsrelying on dense control signals such as edge maps, we provide a more intuitiveand user-friendly interface to allow object-level fine-grained control. Ourmethod achieves controllability of object appearances without finetuning, whichreduces the per-subject optimization efforts for the users. Extensiveexperiments on standard benchmark datasets and user-provided inputs validatethat our model obtains a 70% improvement in controllability metrics overcompetitive baselines.

Fine-grained Controllable Video Generation via Object Appearance and Context