GANs, CNNs and autoencoders began to crop up in headline-grabbing experiments around style-transfer and the inference and generation, using data from the public domain, of ‘unreal’, yet photorealistic images. Almost overnight (in terms of the often-hindered history of AI), a great deal of global governmental and industry-led research into computer vision and object recognition, research centered around well-funded sectors such as robotics, logistics and security footage analysis, had become actionable and accessible to less ‘serious’ purposes. Recent advances in GPU-based machine learning had begun to facilitate the processing of large amounts of data in increasingly efficient time-frames.
Something seismic had begun to occur in machine learning research in this period.
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This apparent alchemy, now packaged by a pseudonymous user into a public code repository called DeepFakes, had been achieved with the use of a Convolutional Neural Network ( CNN) and autoencoders (but not, as widely reported in mainstream articles and in Wikipedia, using a Generative Adversarial Network – a machine learning technique first proposed by lead Google researcher Ian Goodfellow over three years earlier which was then gaining traction in other image-generation projects ¹ ). In that period a new sub-Reddit appeared, dedicated to publishing short pornographic video clips which had been convincingly altered to feature the faces of celebrities. In late 2017, not for the first time, porn proved a prime mover for a relatively obscure new technology. And that a new ‘disruptive event’ may be coming to shake up the industry. It now seems that the water is rippling again for the current state of the art in visual effects, as new machine learning techniques slowly encroach on the now-established workflows of CGI.
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Many influential movie makers and VFX studios were unable or unwilling to read the signs of the times in the years leading up to Jurassic Park. It set the trend for the decades ahead, and reinvented the visual effects industry - not without many casualties among the old guard. In the few years directly prior, James Cameron had brought renewed interest to the possibilities of CGI with the ‘liquid’ effects of The Abyss and Terminator 2: Judgement Day.īut Jurassic Park was different: computers had achieved the ability to generate solid, photo-real objects, promising to relegate the uncomfortable burdens of the physical, photochemical VFX world. They had been presaged sporadically throughout the 1970s, and at greater length in the 1980s, in cinematic curios such as Tron, The Last Starfighter and Flight of The Navigator. Though a quantum leap, the visual effects of Jurassic Park did not represent an overnight upheaval. When Steven Spielberg showed the first rushes of computer-generated dinosaurs to acclaimed traditional stop-motion animator Phil Tippett (who had been hired to create the dinosaurs in the same way they had been done since the 1920s) he announced “I think I’m extinct.” It’s a line so significant that it made it into the movie itself, in reference to a paleontologist envisaging a world where no-one would need him to theorize about dinosaurs any longer. It’s over twenty five years since the ground-breaking CGI effects of Jurassic Park usurped 100 years of visual effects tradition. New machine learning techniques being pioneered at the major visual effects studios promise to transform the visual effects industry in a way not seen since the CGI revolution.Ī lot has changed in the VFX AI scene since this article was published in early 2019, so do also check out our more recent feature on The Future Of AI Image Synthesis
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