Game Art Institute Digital Realism The Face

Game Art Institute Digital Realism The Face

LINK ===== https://urlca.com/2trr9X

ImagineFX is the No.1 selling digital art magazine for fantasy and sci-fi enthusiasts! Featuring digital and traditional drawing skills, game design, manga and film art each issue is crammed with training and inspiration from leading artists in their fields. Whether it's learning from comic art's Adam Hughes, fantasy art's John Howe, or digital painting's Loish, ImagineFX has you covered. ImagineFX has been inspiring artists for 15 years!\"}; var triggerHydrate = function() { window.sliceComponents.authorBio.hydrate(data, componentContainer); } var triggerScriptLoadThenHydrate = function() { var script = document.createElement('script'); script.src = ' -8-2/authorBio.js'; script.async = true; script.id = 'vanilla-slice-authorBio-component-script'; script.onload = () => { window.sliceComponents.authorBio = authorBio; triggerHydrate(); }; document.head.append(script); } if (window.lazyObserveElement) { window.lazyObserveElement(componentContainer, triggerScriptLoadThenHydrate); } else { triggerHydrate(); } } }).catch(err => console.log('Hydration Script has failed for authorBio Slice', err)); }).catch(err => console.log('Externals script failed to load', err));ImagineFX staffSocial Links NavigationImagineFX is the No.1 selling digital art magazine for fantasy and sci-fi enthusiasts! Featuring digital and traditional drawing skills, game design, manga and film art each issue is crammed with training and inspiration from leading artists in their fields. Whether it's learning from comic art's Adam Hughes, fantasy art's John Howe, or digital painting's Loish, ImagineFX has you covered. ImagineFX has been inspiring artists for 15 years!

Recently, digital face manipulation and its detection have sparked large interest in industry and academia around the world. Numerous approaches have been proposed in the literature to create realistic face manipulations, such as DeepFakes and face morphs. To the human eye manipulated images and videos can be almost indistinguishable from real content. Although impressive progress has been reported in the automatic detection of such face manipulations, this research field is often considered to be a cat and mouse game. This chapter briefly discusses the state of the art of digital face manipulation and detection. Issues and challenges that need to be tackled by the research community are summarized, along with future trends in the field.

Over the last couple of years, digital face manipulation and detection has become a highly active area of research. This is demonstrated through the increasing number of workshops in top conferences [1,2,3,4,5], international projects such as MediFor and the recent SemaFor funded by the Defense Advanced Research Project Agency (DARPA), and competitions such as the Media Forensics Challenge (MFC2018)Footnote 1 launched by the National Institute of Standards and Technology (NIST), the Deepfake Detection Challenge (DFDC)Footnote 2 launched by Facebook, and the recent DeeperForensics Challenge.Footnote 3

Motivated by those facts, researchers have proposed various techniques to detect digital face manipulations in the recent past [10, 11]. In addition, public databases have been made available and first benchmarks have been conducted by different research groups [12,13,14,15,16,17], proving the high potential of the latest manipulation detectors. Nonetheless, a reliable detection of manipulated face images and videos is still considered an unsolved problem. It is generally conceded that digital face manipulation detection is still a nascent field of research in which numerous issues and challenges have to be addressed in order to reliably deploy such methods in real-world applications.

Blending manipulated faces into the original image or video: although there have been improvements in the blending algorithms [19], artifacts at the edges of the manipulated and original regions still exist in many cases. In addition, mismatch between these two regions (e.g., lighting condition, skin color, or noise) can degrade the realism of the manipulated images/videos, making them easier to be detected.

Tackling the unknown emerging face manipulations is still a key challenge [30, 39]. In fact, generalization of detection techniques is crucial in attaining dependable accuracy in real-life scenarios. It is agreed upon researchers that face manipulation and detection is well described as a cat and mouse game, where improvements in one area trigger improvements in the other.

Beyond adversarial attacks, it is worth observing that every detection algorithm should take into account the presence of an adversary to fool it. In fact, by relying on the knowledge of the specific clues exploited by a face manipulation detector, one can make it not work anymore. For example, if an adversary knows that the algorithm exploits the presence of the specific GAN fingerprints that characterize synthetic media, then it would be possible to remove them [30] and also to insert real fingerprints related to modern digital cameras [52]. Overall, researchers should be always aware about the two-player nature of this research and design a detector robust also to possible targeted attacks.

This concluding chapter has given an overview of different unsolved issues in (and surrounding) the research field of digital face manipulation and detection. It summarizes the opinions of several distinguished researchers from academia and industry of different backgrounds, including computer vision, pattern recognition, media forensics as well as social and legal research, regarding the future trends in said field. Moreover, this chapter has listed various avenues which should be considered in future research and, thus, serves as good reference point for researchers working in the area of digital face manipulation and detection.

A series of studies experimentally investigated whether uncanny valley effects exist for static images of robot faces. Mathur MB & Reichling DB[17] used two complementary sets of stimuli spanning the range from very mechanical to very human-like: first, a sample of 80 objectively chosen robot face images from Internet searches, and second, a morphometrically and graphically controlled 6-face series set of faces. They asked subjects to explicitly rate the likability of each face. To measure trust toward each face, subjects completed a one-shot investment game to indirectly measure how much money they were willing to \"wager\" on a robot's trustworthiness. Both stimulus sets showed a robust uncanny valley effect on explicitly-rated likability and a more context-dependent uncanny valley on implicitly-rated trust. Their exploratory analysis of one proposed mechanism for the uncanny valley, perceptual confusion at a category boundary, found that category confusion occurs in the uncanny valley but does not mediate the effect on social and emotional responses.

One study conducted in 2009 examined the evolutionary mechanism behind the aversion associated with the uncanny valley. A group of five monkeys were shown three images: two different 3D monkey faces (realistic, unrealistic), and a real photo of a monkey's face. The monkeys' eye-gaze was used as a proxy for preference or aversion. Since the realistic 3D monkey face was looked at less than either the real photo, or the unrealistic 3D monkey face, this was interpreted as an indication that the monkey participants found the realistic 3D face aversive, or otherwise preferred the other two images. As one would expect with the uncanny valley, more realism can lead to less positive reactions, and this study demonstrated that neither human-specific cognitive processes, nor human culture explain the uncanny valley. In other words, this aversive reaction to realism can be said to be evolutionary in origin.[30]

Viewer perception of facial expression and speech and the uncanny valley in realistic, human-like characters intended for video games and film is being investigated by Tinwell et al., 2011.[35] Consideration is also given by Tinwell et al. (2010) as to how the uncanny may be exaggerated for antipathetic characters in survival horror games.[36] Building on the body of work already undertaken in android science, this research intends to build a conceptual framework of the uncanny valley using 3D characters generated in a real-time gaming engine. The goal is to analyze how cross-modal factors of facial expression and speech can exaggerate the uncanny. Tinwell et al., 2011[37] have also introduced the notion of an 'unscalable' uncanny wall that suggests that a viewer's discernment for detecting imperfections in realism will keep pace with new technologies in simulating realism. A summary of Angela Tinwell's research on the uncanny valley, psychological reasons behind the uncanny valley and how designers may overcome the uncanny in human-like virtual characters is provided in her book, The Uncanny Valley in Games and Animation by CRC Press.

The use of virtual actors is in contrast with digital de-aging, which can involve simply removing wrinkles from actors' faces. This practice has generally not faced uncanny valley criticism. One exception is the 2019 film The Irishman, in which Robert De Niro, Al Pacino and Joe Pesci were all de-aged to try to make them look up to 50 years younger: one reviewer wrote that the actors' \"hunched and stiff\" body language stood in marked contrast to their facial appearance,[100] while another wrote that when De Niro's character was in his 30s, he looked like he was 50.[101]

My main goals with the course were to update myself with the latest workflows and pipelines when it comes to character art for games, and also push my quality bar across various areas within the craft. Anatomy, realism, texturing, getting shaders to look good, and presentation. I also wanted to get an insight into what is coming up on the horizon of real-time character art, since next-generation games are just around the corner. 1e1e36bf2d