Converting Motion Capture Into Editable Keyframe Animation: Fast, Optimal, and Generic Keyframe Selection

<p>Motion capture is attractive to visual effects studios because it offers a fast and automatic way to create animation directly from actors' movements. Despite extensive research efforts toward motion capture processing and motion editing, animations created using motion capture are notoriously difficult to edit. We investigate this problem and develop a technique to reverse engineer editable keyframe animation from motion capture. Our technique for converting motion capture into editable animation is to select keyframes from the motion capture that correspond to those an animator might have used to create the motion from scratch. As the first contribution presented by this thesis, we survey both traditional and contemporary animation practice to define the types of keyframes created by animators following conventional animation practices. As the second contribution, we develop a new keyframe selection algorithm that uses a generic objective function; using different implementations, we can define different criteria to which keyframes are selected. After presenting the algorithm, we return to the problem of converting motion capture into editable animation and design three implementations of the objective function that can be used together to select animator-like keyframes. Finally, as a minor contribution to conclude the thesis, we present a simple interpolation algorithm that can be used to construct a new animation from only the selected keyframes. In contrast to previous research in the topic of keyframe selection, our technique is novel in that we have designed it to provide selections of keyframes that are similar in structure to those used by animators following conventional practices. Consequently, both animators and motion editors can adjust the resulting animation in much the same way as their own, manually created, content. Furthermore, our technique offers an optimal guarantee paired with fast performance for practical editing situations, which has not yet been achieved in previous research. In conclusion, the contributions of this thesis advance the state of the art in the topic by introducing the first fast, optimal, and generic keyframe selection algorithm. Ultimately, our technique is not only well suited to the problem of recovering editable animation from motion capture, but can also be used to select keyframes for other purposes - such as compression or pattern identification - provided that an appropriate implementation of the objective function can be imagined and employed.</p>

Converting Motion Capture Into Editable Keyframe Animation: Fast, Optimal, and Generic Keyframe Selection

<p>Motion capture is attractive to visual effects studios because it offers a fast and automatic way to create animation directly from actors' movements. Despite extensive research efforts toward motion capture processing and motion editing, animations created using motion capture are notoriously difficult to edit. We investigate this problem and develop a technique to reverse engineer editable keyframe animation from motion capture. Our technique for converting motion capture into editable animation is to select keyframes from the motion capture that correspond to those an animator might have used to create the motion from scratch. As the first contribution presented by this thesis, we survey both traditional and contemporary animation practice to define the types of keyframes created by animators following conventional animation practices. As the second contribution, we develop a new keyframe selection algorithm that uses a generic objective function; using different implementations, we can define different criteria to which keyframes are selected. After presenting the algorithm, we return to the problem of converting motion capture into editable animation and design three implementations of the objective function that can be used together to select animator-like keyframes. Finally, as a minor contribution to conclude the thesis, we present a simple interpolation algorithm that can be used to construct a new animation from only the selected keyframes. In contrast to previous research in the topic of keyframe selection, our technique is novel in that we have designed it to provide selections of keyframes that are similar in structure to those used by animators following conventional practices. Consequently, both animators and motion editors can adjust the resulting animation in much the same way as their own, manually created, content. Furthermore, our technique offers an optimal guarantee paired with fast performance for practical editing situations, which has not yet been achieved in previous research. In conclusion, the contributions of this thesis advance the state of the art in the topic by introducing the first fast, optimal, and generic keyframe selection algorithm. Ultimately, our technique is not only well suited to the problem of recovering editable animation from motion capture, but can also be used to select keyframes for other purposes - such as compression or pattern identification - provided that an appropriate implementation of the objective function can be imagined and employed.</p>

Optimal keyframe selection-based lossless video-watermarking technique using IGSA in LWT domain for copyright protection

Video piracy is a challenging issue in the modern world. Approximately $$90\%$$ 90 % of newly released films were illegally distributed around the world via the Internet. To overcome this issue, video watermarking is an effective process that integrates a logo in video frames as a watermark. Therefore, this paper presents an efficient lossless video-watermarking scheme based on optimal keyframe selection using an intelligent gravitational search algorithm in linear wavelet transform. This technique obtains color motion and motionless frames from the cover video by the histogram difference method. One-level linear wavelet transform is performed on the chrominance channel of motion frames and a low-frequency sub-band LL opts for watermark embedding. The performance of the proposed technique has been evaluated against 12 video processing attacks in terms of imperceptibility and robustness. Experiments demonstrate that the proposed technique outperforms five state-of-the-art schemes on the considered attacks.

Towards Efficient Video Detection Object Super-Resolution with Deep Fusion Network for Public Safety

Video surveillance plays an increasingly important role in public security and is a technical foundation for constructing safe and smart cities. The traditional video surveillance systems can only provide real-time monitoring or manually analyze cases by reviewing the surveillance video. So, it is difficult to use the data sampled from the surveillance video effectively. In this paper, we proposed an efficient video detection object super-resolution with a deep fusion network for public security. Firstly, we designed a super-resolution framework for video detection objects. By fusing object detection algorithms, video keyframe selection algorithms, and super-resolution reconstruction algorithms, we proposed a deep learning-based intelligent video detection object super-resolution (SR) method. Secondly, we designed a regression-based object detection algorithm and a key video frame selection algorithm. The object detection algorithm is used to assist police and security personnel to track suspicious objects in real time. The keyframe selection algorithm can select key information from a large amount of redundant information, which helps to improve the efficiency of video content analysis and reduce labor costs. Finally, we designed an asymmetric depth recursive back-projection network for super-resolution reconstruction. By combining the advantages of the pixel-based super-resolution algorithm and the feature space-based super-resolution algorithm, we improved the resolution and the visual perception clarity of the key objects. Extensive experimental evaluations show the efficiency and effectiveness of our method.