Human pose recognition and its generation are an important animation design key point. To this end, this paper designs new neural network structures for 2D and 3D pose extraction tasks and corresponding GPU-oriented acceleration schemes. The scheme first takes an image as input, extracts the human pose from it, converts it into an abstract pose data structure, and then uses the converted dataset as a basis to generate the desired character animation based on the input at runtime. The scheme in this paper has been tested on pose recognition datasets and different levels of hardware showing that 2D pose recognition can reach speeds above 60 fps on common computer hardware, 3D pose recognition can be estimated to reach speeds above 24 fps with an average error of only 110 mm, and real-time animation generation can reach speeds above 30 frames per second.
The traditional style migration of animation costumes is mainly performed between two paired animation costumes. However, the generalization ability is weak, and the migration effect is not good when the gap between the training and testing costumes is large. To address the above problems, this paper proposes a style migration method for animated costumes combining full convolutional network (FCN) and CycleGAN, which enables the instance style migration between animated costumes with specific targets. It is also verified that the training dataset is not the factor that causes the poor style migration of CycleGAN. The experiments demonstrate that the animation costume style migration method combining full convolutional network and CycleGAN increases the recognition ability and can achieve the local style migration of the animation costume while maintaining the integrity of the rest of the elements, and compared with CycleGAN, the method can effectively suppress the style migration in areas outside the target.
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