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Opinion, Americas, Special Report
#1
Sales of the NES were phenomenal. This established Nintendo as the dominant home video game manufacturer until the late '90s, when it was eclipsed by the rival Sony PlayStation. In 1989, Nintendo introduced a new 16-bit system dubbed the Super Nintendo Entertainment System (SNES). Within a couple of years, rivals had introduced 32-bit systems that eclipsed the capabilities of the SNES. So, Nintendo announced an agreement with Silicon Graphics Inc. (SGI) to develop a new 64-bit video game system, code-named Project Reality. Although SGI had never designed video game hardware before, the company was regarded as one of the leaders in computer graphics technology. But the delays and shortage of games during the first year of availability gave the advantage to Sony, who had released the PlayStation over a year earlier. Nintendo 64 uses a customized chip system. Let's take a look at the components inside an N64, and what their capabilities are.|Unpaired video-to-video translation aims to translate videos between a source and a target domain without the need of paired training data, making it more feasible for real applications. Unfortunately, the translated videos generally suffer from temporal and semantic inconsistency. To address this, many existing works adopt spatiotemporal consistency constraints incorporating temporal information based on motion estimation. However, the inaccuracies in the estimation of motion deteriorate the quality of the guidance towards spatiotemporal consistency, which leads to unstable translation. In this work, we propose a novel paradigm that regularizes the spatiotemporal consistency by synthesizing motions in input videos with the generated optical flow instead of estimating them. Therefore, the synthetic motion can be applied in the regularization paradigm to keep motions consistent across domains without the risk of errors in motion estimation. Thereafter, we utilize our unsupervised recycle and unsupervised spatial loss, guided by the pseudo-supervision provided by the synthetic optical flow, to accurately enforce spatiotemporal consistency in both domains. Experiments show that our method is versatile in various scenarios and achieves state-of-the-art performance in generating temporally and semantically consistent videos.




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