Support Vector Machine Wavelet Blind Equalization Algorithm Based on Improved Genetic Algorithm

Guo, Yecai; Li, Baoge; Fan, Kang

doi:10.1007/978-3-642-28658-2_24

Cited by 2 publications

(1 citation statement)

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“…Treating the human key point connection relationship as a graph structure is a prerequisite for implementing graph convolution, and then, the 3D relationship of bones can be extracted from global and local together. Previous approaches focus on feature learning and large-scale data training on pixel space, without making good use of a priori information such as human kinematics, spatial-physical relationships, and human topology [9], and for monocular 3D pose, inference relying on neural network learning alone is not sufficient. Some common problems, such as spatial relative position errors of multiple targets, contact with the ground with penetration and vacillation, unnatural tilt of pose and mutual occlusion, are found from the practical use of some models.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Motion Capture Technology in Film and Television Animation Production

Wei

2022

Security and Communication Networks

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With the popularity of King Kong, Pirates of the Caribbean 2, Avatar, and other films, the virtual characters in these films have become popular and well loved by audiences. The creation of these virtual characters is different from traditional 3D animation but is based on real character movements and expressions. An overview of several mainstream motion capture systems in the field of motion capture is presented, and the application of motion capture technology in film and animation is explained in detail. The current motion capture technology is mainly based on complex human markers and sensors, which are costly, while deep-learning-based human pose estimation is becoming a new option. However, most existing methods are based on a single person or picture estimation, and there are many challenges for video multiperson estimation. The experimental results show that a simple design of the human motion capture system is achieved.

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Section: Introductionmentioning

confidence: 99%