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Article summary:
1. A cascade GAN approach is proposed to generate talking face video, which is robust to different face shapes, view angles, facial characteristics, and noisy audio conditions.
2. A novel dynamically adjustable pixel-wise loss with an attention mechanism is proposed to avoid pixel jittering problems and enforce the network to focus on audiovisual-correlated regions.
3. A novel regression-based discriminator structure is proposed to generate a sharper image with well-synchronized facial movements.
Article analysis:
The article “Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss” presents a novel approach for generating talking face videos using a cascade GAN approach. The authors claim that their method is robust to different face shapes, view angles, facial characteristics, and noisy audio conditions. They also propose a novel dynamically adjustable pixel-wise loss with an attention mechanism and a novel regression-based discriminator structure in order to generate sharper images with well-synchronized facial movements.
The article appears to be reliable and trustworthy as it provides detailed information about the methods used and the results obtained from experiments conducted on several datasets and real world samples. The authors have provided evidence for their claims by providing quantitative and qualitative comparisons of their results against those of state of the art methods. Furthermore, they have discussed potential risks associated with their method such as pixel jittering problems which can be avoided by using their proposed dynamic pixel wise loss with an attention mechanism.
In conclusion, this article appears to be reliable and trustworthy as it provides detailed information about the methods used and the results obtained from experiments conducted on several datasets and real world samples. The authors have provided evidence for their claims by providing quantitative and qualitative comparisons of their results against those of state of the art methods.