Bing Liu’s scientific contributions

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Publications (1)


Flowchart of animation design based on two-layer RBM model.
Total error of RBM reconstruction in layer 1 versus the number of iterations. (a) Dataset 1. (b) Dataset 2.
Total error of RBM reconstruction in layer 1 versus the number of iterations. (a) Dataset 1. (b) Dataset 2.
Effect of end effector reconstruction. (a) Left foot. (b) Right foot. (c) Left hand. (d) Right hand.
Effect of end effector reconstruction. (a) Left foot. (b) Right foot. (c) Left hand. (d) Right hand.

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Research on Virtual Interactive Animation Design System Based on Deep Learning
  • Article
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June 2022

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5 Citations

Computational Intelligence and Neuroscience

Bing Liu

With the rapid development of computer network technology, the advantages of virtual reality technology in the field of instant messaging are becoming more and more significant. Virtual reality technology plays an important role in communication networks, including enhanced resource utilization, device redundancy, immersion, interactivity, conceptualization, and holography. In this paper, we use the basic theory of Restricted Boltzmann Machine to establish a semisupervised spatio-temporal feature model through the animation capture data style recognition problem. The bottom layer can be pretrained with a large amount of unlabeled data to enhance the model’s feature perception capability of animation data, and then train the high-level supervised model with the labeled data to finally obtain the model parameters that can be used for the recognition task. The layer-by-layer training method makes the model have good parallelism, that is, when the layer-by-layer training method makes the model well parallelized, that is, when the bottom features cannot effectively represent the animation features, such as overfitting or underfitting, only the bottom model needs to be retrained, while the top model parameters can be kept unchanged. Simulation experiments show that the design assistance time of this paper’s scheme for animation is reduced by 10 minutes compared to baseline.

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Citations (1)


... The article showed that the accuracy, quantity, and rate of motion of models have all been significantly enhanced by the 3D modeling technique based on depth photographs [23]. The study examines that, whenever the bottom characteristics, such as overfitting or regression problems, cannot accurately reflect the animating characteristics, the texture learning model makes the model adequately parallelized, meaning that the top model variables may be left unaltered and only the bottom model has to be reassigned [24]. The research suggested a design-oriented learning approach, the Advanced Animation Teaching Model. ...

Reference:

Practical Analysis of Virtual Reality 3D Modeling Technology for Animation Majors Based on Predictive Correction Method
Research on Virtual Interactive Animation Design System Based on Deep Learning

Computational Intelligence and Neuroscience