Video synopsis is an efficient method for condensing surveillance videos. This technique begins with the detection and tracking of objects, followed by the creation of object tubes. These tubes consist of sequences, each containing chronologically ordered bounding boxes of a unique object. To generate a condensed video, the first step involves rearranging the object tubes to maximize the number of non-overlapping objects in each frame. Then, these tubes are stitched to a background image extracted from the source video. The lack of a standard dataset for the video synopsis task hinders the comparison of different video synopsis models. This paper addresses this issue by introducing a standard dataset, called SynoClip, designed specifically for the video synopsis task. SynoClip includes all the necessary features needed to evaluate various models directly and effectively. Additionally, this work introduces a video synopsis model, called FGS, with low computational cost. The model includes an empty-frame object detector to identify frames empty of any objects, facilitating efficient utilization of the deep object detector. Moreover, a tube grouping algorithm is proposed to maintain relationships among tubes in the synthesized video. This is followed by a greedy tube rearrangement algorithm, which efficiently determines the start time of each tube. Finally, the proposed model is evaluated using the proposed dataset. The source code, fine-tuned object detection model, and tutorials are available at https://github.com/Ramtin-ma/VideoSynopsis-FGS.
Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. At the same time, scaling these models to enable longer, more dynamic video synthesis often remains computationally intractable. To address this challenge, we introduce the concept of Generative Temporal Nursing (GTN), where we aim to alter the generative process on the fly during inference to improve control over the temporal dynamics and enable generation of longer videos. We propose a method for GTN, dubbed VSTAR, which consists of two key ingredients: 1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis based on the original single prompt leveraging LLMs, which gives accurate textual guidance to different visual states of longer videos, and 2) Temporal Attention Regularization (TAR) - a regularization technique to refine the temporal attention units of the pre-trained T2V diffusion models, which enables control over the video dynamics. We experimentally showcase the superiority of the proposed approach in generating longer, visually appealing videos over existing open-sourced T2V models. We additionally analyze the temporal attention maps realized with and without VSTAR, demonstrating the importance of applying our method to mitigate neglect of the desired visual change over time.
We present a minimal phase oscillator model for learning quadrupedal locomotion. Each of the four oscillators is coupled only to itself and its corresponding leg through local feedback of the ground reaction force, which can be interpreted as an observer feedback gain. We interpret the oscillator itself as a latent contact state-estimator. Through a systematic ablation study, we show that the combination of phase observations, simple phase-based rewards, and the local feedback dynamics induces policies that exhibit emergent gait preferences, while using a reduced set of simple rewards, and without prescribing a specific gait. The code is open-source, and a video synopsis available at https://youtu.be/1NKQ0rSV3jU.