Diverse single image generation with controllable global structure

Mahendren, Sutharsan; Edussooriya, Chamira U. S.; Rodrigo, Ranga

doi:10.1016/j.neucom.2023.01.011

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…To validate the animation production effect of the model described in this research, we incorporated a similar model for experimental comparison. The comparison models are GAN, CycleGAN, Ref [15], Ref [16], Ref [17]. The facial animation generation outcomes achieved from each model are presented in Table 2.…”

Section: Animation Generation Performance Experimentsmentioning

confidence: 99%

“…The comparison of the generation speed of each model is shown in Figure 5, and FPS stands for Frames Per Second. three reference models, Reference [15], Reference [16] and Reference [17], are 23.9, 21.3 and 22.6, respectively.They all show a decreasing trend compared to GAN and CycleGAN, showing that in the experimental The use of different model structures and algorithms may lead to a decrease in generation speed.This Model achieves a score of 24.5 in terms of FPS, which is a better overall performance relative to the reference model. Despite the introduction of the multi-attention mechanism, this paper's model seems to better balance image quality while maintaining efficient generation speed.…”

Section: Animation Generation Time Experimentmentioning

confidence: 99%

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A Digital Animation Generation Model Based on Cycle Adversarial Neural Network

Xu,

Zhang

et al. 2024

J. Phys.: Conf. Ser.

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This paper proposes a digital animation generation model based on Cycle Adversarial Neural Network (CycleGAN). Compared with the classical CycleGAN, this research presents a multi-attention approach to enhance the network’s generalization. Specifically, a style enhancement module and a style cross-attention mechanism are introduced into the generator network structure, which enables the model to better parse the structural information of the content image and realize the accurate matching of content features and style features. Furthermore, the introduction of a multi-scale discriminator with a fused attention mechanism enhances the preservation of content information from the source image in the output animated image. The experiments conducted in this study demonstrate that the model proposed in this paper exhibits superior performance in generating digital animation. This model not only enhances the realism and variety of the generated effects, but also achieves significant improvements in the coherence and stability of long time-series animation. The contribution of this paper lies in the introduction of the multi-attention mechanism, which enhances the generalization of the network and is of great theoretical and practical significance for the development of the field of digital animation generation.

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Section: Animation Generation Performance Experimentsmentioning

confidence: 99%