Special Report, Americas, World News

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Instead, it will be built into a tough but pliable fabric frame. Even the screen will able to bend and fold and will have a thickness and flexibility similar to a laminated sheet of paper. Furthermore, how could it be predicted to hit the market in just a few years? Experts say it will be available in 2 to 10 years. Find out why this technology really is right around the corner and why some of its seemingly futuristic features aren't actually so different from what's being offered by products available on the market today. Keep reading to lear­n how a computer could be built within a flexible fabric frame instead of a more traditional rigid metal housing. Conventional computer hard disk drives are large and bulky, but flash memory, common today in memory sticks and cameras, is a possible lightweight alternative for memory storage in the laptops of tomorrow. To understand how a laptop computer could be designed to be flexible, let's first consider why conventional laptops aren't flexible.|Advanced face swapping methods have achieved appealing results. However, most of these methods have many parameters and computations, which makes it challenging to apply them in real-time applications or deploy them on edge devices like mobile phones. In this work, we propose a lightweight Identity-aware Dynamic Network (IDN) for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information. In particular, we design an efficient Identity Injection Module (IIM) by introducing two dynamic neural network techniques, including the weights prediction and weights modulation. Once the IDN is updated, it can be applied to swap faces given any target image or video. The presented IDN contains only 0.50M parameters and needs 0.33G FLOPs per frame, making it capable for real-time video face swapping on mobile phones. In addition, we introduce a knowledge distillation-based method for stable training, and a loss reweighting module is employed to obtain better synthesized results. Finally, our method achieves comparable results with the teacher models and other state-of-the-art methods.




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