Recent large-scale pre-trained diffusion models have demonstrated a powerful generative ability to produce high-quality videos from detailed text descriptions. However, exerting control over the motion of objects in videos generated by any video diffusion model is a challenging problem. In this paper, we propose a novel zero-shot moving object trajectory control framework, Motion-Zero, to enable a bounding-box-trajectories-controlled text-to-video diffusion model. To this end, an initial noise prior module is designed to provide a position-based prior to improve the stability of the appearance of the moving object and the accuracy of position. In addition, based on the attention map of the U-net, spatial constraints are directly applied to the denoising process of diffusion models, which further ensures the positional and spatial consistency of moving objects during the inference. Furthermore, temporal consistency is guaranteed with a proposed shift temporal attention mechanism. Our method can be flexibly applied to various state-of-the-art video diffusion models without any training process. Extensive experiments demonstrate our proposed method can control the motion trajectories of objects and generate high-quality videos.

翻译：近期的大规模预训练扩散模型已展现出强大的生成能力，可根据详细文本描述生成高质量视频。然而，如何控制任意视频扩散模型生成视频中对象的运动轨迹仍是具有挑战性的问题。本文提出一种新颖的零样本运动对象轨迹控制框架Motion-Zero，实现了边界框轨迹约束的文生视频扩散模型。为此，我们设计了初始噪声先验模块，通过提供基于位置的先验信息来提升运动对象外观稳定性与位置精度。此外，基于U-Net的注意力图，我们将空间约束直接作用于扩散模型的去噪过程，进一步保证了推理阶段运动对象的位置一致性与空间连续性。同时，通过提出的移位时序注意力机制确保时序一致性。该方法无需训练过程即可灵活应用于各类先进视频扩散模型。大量实验表明，本方法能有效控制对象的运动轨迹并生成高质量视频。