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<p>Given the common industry problems in 3D animation production, such as strong noise interference in visual capture data, stiff action generation, and lack of physical reality, this study constructs an automated action generation framework that combines machine vision and an improved simulated annealing algorithm. This method uses computational intelligence to achieve accurate mapping of low-cost videos to high-quality animations. It first uses a deep learning pose estimation model initialized with pre-trained weights and adapted to the target animation-motion task to extract 17-joint temporal features, and then builds a skeletal reorientation mechanism based on quaternion transformation. Meanwhile, an adaptive hyperbolic tangent cooling strategy and a multi-physics constrained energy function including smoothness and anatomical constraints are introduced to drive the algorithm to perform nonlinear global optimization in complex animation configuration spaces. The results showed that the average joint position error of this method on the Human3.6&#xa0;M dataset was stable in the range of 47.9&#xa0;mm to 55.4&#xa0;mm, and the comprehensive evaluation score reached 0.89. The optimized action sequence joint acceleration was significantly reduced to 2.4, and single-frame inference only took 48ms, which effectively eliminates visual jitter and foot slipping artifacts while ensuring real-time operation efficiency. The research results successfully solved the pain point of difficulty in direct application of monocular visual data, and provided a high-fidelity technical paradigm for digital media content production that takes into account physical consistency and natural motion.</p>

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Analysis of animation character action design based on machine vision and improved simulated annealing algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月23日

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  • Yajie Lin,
  • Jianqi Hong,
  • Zonghua Tao

摘要

Given the common industry problems in 3D animation production, such as strong noise interference in visual capture data, stiff action generation, and lack of physical reality, this study constructs an automated action generation framework that combines machine vision and an improved simulated annealing algorithm. This method uses computational intelligence to achieve accurate mapping of low-cost videos to high-quality animations. It first uses a deep learning pose estimation model initialized with pre-trained weights and adapted to the target animation-motion task to extract 17-joint temporal features, and then builds a skeletal reorientation mechanism based on quaternion transformation. Meanwhile, an adaptive hyperbolic tangent cooling strategy and a multi-physics constrained energy function including smoothness and anatomical constraints are introduced to drive the algorithm to perform nonlinear global optimization in complex animation configuration spaces. The results showed that the average joint position error of this method on the Human3.6 M dataset was stable in the range of 47.9 mm to 55.4 mm, and the comprehensive evaluation score reached 0.89. The optimized action sequence joint acceleration was significantly reduced to 2.4, and single-frame inference only took 48ms, which effectively eliminates visual jitter and foot slipping artifacts while ensuring real-time operation efficiency. The research results successfully solved the pain point of difficulty in direct application of monocular visual data, and provided a high-fidelity technical paradigm for digital media content production that takes into account physical consistency and natural motion.