TempoControl: Temporal Attention Guidance for Text-to-Video Models
Summary: arXiv:2510.02226v3 Announce Type: replace-cross
Abstract: Recent advances in generative video models have enabled the creation of high-quality videos based on natural language prompts. However, these models frequently lack fine-grained temporal control, meaning they do not allow users to specify when particular visual elements should appear within a generated sequence. In this work, we introduce TempoControl, a method that allows for temporal alignment of visual concepts during inference, without requiring retraining or additional supervision.
Introduction
The evolution of generative video models has been a remarkable journey, opening doors to innovative applications in content creation, entertainment, and education. However, the challenge of achieving precise temporal control in these models has persisted, limiting their utility in scenarios where timing is crucial. TempoControl addresses this gap by introducing an intuitive method for guiding the timing of visual concepts in generated videos.
Methodology
TempoControl leverages cross-attention maps, which are integral to text-to-video diffusion models, to facilitate the temporal alignment of visual elements. This is achieved through a novel optimization approach that steers attention using three essential principles:
- Correlation: Aligning the temporal pattern of the attention mechanism with a control signal to specify when certain elements should be visually represented.
- Magnitude: Adjusting the strength of attention to ensure visibility at critical moments in the video.
- Entropy: Preserving semantic consistency throughout the video to maintain coherence in the narrative and visual design.
Applications
TempoControl demonstrates its versatility across various applications, effectively enhancing the capabilities of text-to-video models. Some key applications include:
- Temporal Reordering: Allowing users to specify the order in which single or multiple objects appear, adding flexibility to video generation.
- Action Timing: Providing precise control over when actions occur within a scene, enhancing storytelling effectiveness.
- Audio-Aligned Video Generation: Enabling the synchronization of visual elements with audio cues, creating a more immersive viewing experience.
Results and Conclusion
The results from the implementation of TempoControl show significant improvements in both the quality and diversity of generated videos. By offering users the ability to exert fine-grained control over temporal aspects without the need for retraining or additional supervision, TempoControl sets a new standard for text-to-video models.
This innovative approach not only enhances user experience but also expands the creative possibilities in video production, making it a valuable tool for artists, educators, and content creators alike.
Project Page
For more information about TempoControl, visit the project page: TempoControl Project Page.