Advances in text-to-image (T2I) generative models have been spectacular. Recently, text-to-video (T2V) systems have made significant advances, enabling the automatic generation of videos based on textual descriptions. A major challenge in video synthesis is the large amount of memory and training data required. Methods based on the pre-trained stable diffusion (SD) model have been proposed to address efficiency issues in text-to-video (T2V) synthesis.
These approaches approach the problem from several perspectives, including tuning and zero-shot learning. However, text cues should provide better control over the spatial arrangement and trajectories of objects in the generated video. Existing work has addressed this problem by providing low-level control signals, for example, using Canny edge maps or tracked skeletons to guide objects in the video using ControlNet Zhang and Agrawala. These methods achieve good controllability but require considerable effort to produce the control signal.
Capturing the desired movement of an animal or expensive object would be quite difficult, while drawing the desired movement frame by frame would be tedious. To address the needs of casual users, NVIDIA researchers present a high-level interface for controlling object trajectories in synthesized videos. Users must provide bounding boxes (bboxes) that specify the desired position of an object at various points in the video, along with text cues that describe the object at the corresponding times.
Their strategy involves editing spatial and temporal attention maps for a specific object during the initial diffusion denoising steps to focus activation on the desired object location. Its inference-time editing approach achieves this without disrupting the text-image association learned in the pre-trained model and requires minimal code modifications.
Their approach allows users to position the subject by keyframing its bounding box. The size of the bbox can be controlled in a similar way, thus producing perspective effects. Finally, users can also keyframe the text message to influence the behavior of the subject in the synthesized video.
By animating bounding boxes and messages through keyframes, users can modify the trajectory and basic behavior of the subject over time. This facilitates the seamless integration of the resulting themes into a specific environment, providing an accessible video storytelling tool for casual users.
Their approach does not require any model tuning, training or online optimization, ensuring computational efficiency and excellent user experience. Finally, his method produces natural results, automatically incorporating desirable effects such as perspective, precise movement of objects, and interactions between objects and their environment.
However, their method inherits common failure cases from the underlying diffusion model, including challenges with deformed objects and difficulties generating multiple objects with precise attributes such as color.
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Arshad is an intern at MarktechPost. He is currently pursuing his international career. Master's degree in Physics from the Indian Institute of technology Kharagpur. Understanding things down to the fundamental level leads to new discoveries that lead to the advancement of technology. He is passionate about understanding nature fundamentally with the help of tools such as mathematical models, machine learning models, and artificial intelligence.
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