Trajectory Attention for Fine-grained Video Motion Control

Abstract: Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for fine-grained camera motion control. Unlike existing methods that often yield imprecise outputs or neglect temporal correlations, our approach possesses a stronger inductive bias that seamlessly injects trajectory information into the video generation process. Importantly, our approach models trajectory attention as an auxiliary branch alongside traditional temporal attention. This design enables the original temporal attention and the trajectory attention to work in synergy, ensuring both precise motion control and new content generation capability, which is critical when the trajectory is only partially available. Experiments on camera motion control for images and videos demonstrate significant improvements in precision and long-range consistency while maintaining high-quality generation. Furthermore, we show that our approach can be extended to other video motion control tasks, such as first-frame-guided video editing, where it excels in maintaining content consistency over large spatial and temporal ranges.

• The paper introduces trajectory attention to precisely control pixel trajectories in video generation, enhancing camera motion accuracy.
• It employs a dual-branch approach that combines traditional temporal attention with trajectory-focused processing for improved long-range stability.
• Numerical evaluations using metrics like ATE and RPE demonstrate significant improvements in maintaining camera path consistency over time.

Trajectory Attention for Fine-grained Video Motion Control

The paper introduces a novel approach for fine-grained video motion control within the context of video generation, leveraging a mechanism termed "trajectory attention." This technique builds on recent advancements in video diffusion models, which have significantly improved video synthesis capabilities. These models, combining state-of-the-art network architectures and temporal attention mechanisms, play a key role in capturing and reproducing dynamic scenes. However, a challenge persists in effectively controlling camera motion to generate view-customized content.

Trajectory attention is a method that focuses attention along discrete pixel trajectories, providing precise control over video generation processes. This mechanism is intended to address the shortcomings of existing approaches which often either yield imprecise outputs or fail to consider temporal correlations effectively. Central to the methodology is the idea of trajectory attention functioning as an auxiliary branch in the video model, complementing traditional temporal attention without interfering with its operational logic. This dual-branch structure allows for both trajectory information and traditional temporal dynamics to be incorporated into the video generation pipeline.

The design creates a synergy between traditional temporal attention, which emphasizes content consistency and short-range dynamics, and the proposed trajectory attention, which extends focus to ensure stability and coherence over long spatio-temporal ranges. By integrating trajectory attention into the network as an additional layer, the model is capable of handling partial trajectories efficiently, providing substantial improvements in motion control precision and maintaining high-quality content generation.

The implications of this approach are multi-fold, offering a robust framework that is extensible to a variety of applications including camera motion control in both static and dynamic scenarios and video editing tasks that require consistent preservation of content over time. For instance, trajectory attention can be used to maintain consistency in video edits from an altered first frame and to align newly generated content with user-defined trajectories accurately.

Numerical evaluations, as presented in the paper, indicate marked enhancements in the accuracy of trajectory adherence and the overall quality of generated video frames. Metrics such as Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) demonstrate the efficacy of trajectory attention in maintaining camera path fidelity to a greater extent than existing methodologies. It validates this with experimental comparisons against baseline and state-of-the-art frameworks, displaying superior performance in task settings involving varied camera paths and video lengths.

Future work could explore further integration of trajectory attention across different video generative frameworks and adapt the model to dynamically generate trajectories from auxiliary inputs such as natural language descriptions. Additionally, resulting factors like efficiency in sparse trajectory environments and potential applications in real-world contexts—like virtual reality and interactive media—could be assessed more deeply.

Overall, this paper provides a detailed exposition on highly practical, effective video motion control, leveraging advanced attention mechanisms to tackle the nuanced challenges of trajectory-based video content generation. As the field continues to evolve, such development in trajectory management within video generative models will likely have substantial implications for how digital contents are created and experienced.