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Within recent approaches to text-to-video (T2V) generation, achievingcontrollability in the synthesized video is often a challenge. Typically, thisissue is addressed by providing low-level per-frame guidance in the form ofedge maps, depth maps, or an existing video to be altered. However, the processof obtaining such guidance can be labor-intensive. This paper focuses onenhancing controllability in video synthesis by employing straightforwardbounding boxes to guide the subject in various ways, all without the need forneural network training, finetuning, optimization at inference time, or the useof pre-existing videos. Our algorithm, TrailBlazer, is constructed upon apre-trained (T2V) model, and easy to implement. The subject is directed by abounding box through the proposed spatial and temporal attention map editing.Moreover, we introduce the concept of keyframing, allowing the subjecttrajectory and overall appearance to be guided by both a moving bounding boxand corresponding prompts, without the need to provide a detailed mask. Themethod is efficient, with negligible additional computation relative to theunderlying pre-trained model. Despite the simplicity of the bounding boxguidance, the resulting motion is surprisingly natural, with emergent effectsincluding perspective and movement toward the virtual camera as the box sizeincreases.

TrailBlazer: Trajectory Control for Diffusion-Based Video Generation