TrailBlazer: Trajectory Control for Diffusion-Based Video Generation (2401.00896v2)

Abstract: Within recent approaches to text-to-video (T2V) generation, achieving controllability in the synthesized video is often a challenge. Typically, this issue is addressed by providing low-level per-frame guidance in the form of edge maps, depth maps, or an existing video to be altered. However, the process of obtaining such guidance can be labor-intensive. This paper focuses on enhancing controllability in video synthesis by employing straightforward bounding boxes to guide the subject in various ways, all without the need for neural network training, finetuning, optimization at inference time, or the use of pre-existing videos. Our algorithm, TrailBlazer, is constructed upon a pre-trained (T2V) model, and easy to implement. The subject is directed by a bounding box through the proposed spatial and temporal attention map editing. Moreover, we introduce the concept of keyframing, allowing the subject trajectory and overall appearance to be guided by both a moving bounding box and corresponding prompts, without the need to provide a detailed mask. The method is efficient, with negligible additional computation relative to the underlying pre-trained model. Despite the simplicity of the bounding box guidance, the resulting motion is surprisingly natural, with emergent effects including perspective and movement toward the virtual camera as the box size increases.

• The paper introduces TrailBlazer, which uses bounding boxes as a high-level interface to control object trajectories in video generation.
• It edits spatial and temporal attention maps in a pre-trained diffusion model to facilitate smooth keyframe interpolation and efficient guidance.
• Quantitative evaluations demonstrate competitive performance with natural object movements, though challenges with multi-object generation persist.

Overview

The development of text-to-video (T2V) generation technology has made significant advances, allowing the creation of videos from textual descriptions. A typical challenge in this domain is controllability—ensuring objects follow specific spatial and temporal paths in the generated video. This paper introduces a novel method, named TrailBlazer, which provides high-level control over object trajectories in video synthesis without requiring detailed guidance such as edge maps or in-depth user input.

Methodology

The novelty of TrailBlazer lies in its usage of bounding boxes as a simple and high-level interface to guide object trajectories, an approach that is accessible even to casual users. Instead of relying on detailed masks or complex signals, users only need to provide bounding boxes and text prompts at certain key points in the video. The underlying mechanism relies on editing spatial and temporal attention maps within a pre-trained denoising diffusion model, allowing for trajectory and appearance control of the subject. Keyframing is introduced to interpolate the bounding box positioning and text prompts, creating smooth transitions without extensive computational overhead.

Implementation

TrailBlazer is built upon a pre-existing pre-trained T2V model and requires no additional training or optimization. The edits are applied during the initial denoising stages, effectively guiding the activation towards the desired object location while preserving the learned text-image association. The core algorithm has low complexity and is highly efficient—easily implementable in less than 200 lines of code. A key factor in the approach is careful tuning of parameters such as trailing attention map indices and the number of temporal denoising steps. These considerations are essential for achieving high-quality results that balance the guidance of the subject and the naturalness of its motion.

Results and Evaluations

TrailBlazer yielded surprising results, demonstrating natural object movements and emergent effects such as perspective shifts and objects approaching or receding from the virtual camera. The system's capability was tested in various scenarios including single and multiple subjects and under different environmental conditions. Quantitative evaluations were conducted using metrics such as the Frechet Inception Distance (FID), showcasing comparable or improved performance versus alternative approaches. Despite its strengths, TrailBlazer does have limitations. Challenges with the underlying diffusion model, such as object deformation and generating multiple objects, persist. Nonetheless, this method lays the groundwork for user-friendly and controllable text-to-video synthesis that is expected to evolve with advances in generative models.

For detailed visuals and supportive materials, readers can visit the provided project page, which includes comprehensive ablations and examples of TrailBlazer's capabilities in practice.