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Creating Avatars

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3D Renderings of Humans. Google's Latest Machine Vision Breakthrough. Researchers build one-pixel cameras that can take 3D pictures. A team at the University of Glasgow has developed a camera with only one pixel that can nevertheless make 3D models of objects, including ones featuring light beyond the visible spectrum. It works by having a projector beaming a rapidly shifting black and white pattern -- "a bit like a crossword puzzle" according to Miles Padgett, the professor of optics in charge of the project -- onto an object. With each pattern, the amount of white light that reflects off the object back onto the camera indicates the percentage of the white from the pattern overlaps the shape. An algorithm can combine these readings with the shapes of the patterns into a detailed 2D image. "Four detectors give images, each of which contain shadows, giving us clues about the 3D shape of the object," Padgett said.

"Conventional 3D imaging systems which use multiple digital camera sensors to produce a 3D image from 2D information need to be carefully calibrated to ensure the multi-megapixel images align correctly. Seeing depth through a single lens | Harvard School of Engineering and Applied Sciences. Cambridge, Mass. - August 5, 2013 - Researchers at the Harvard School of Engineering and Applied Sciences (SEAS) have developed a way for photographers and microscopists to create a 3D image through a single lens, without moving the camera. Published in the journal Optics Letters, this improbable-sounding technology relies only on computation and mathematics—no unusual hardware or fancy lenses.

The effect is the equivalent of seeing a stereo image with one eye closed. That's easier said than done, as principal investigator Kenneth B. Crozier, John L. "If you close one eye, depth perception becomes difficult. Offering a workaround, Crozier and graduate student Antony Orth essentially compute how the image would look if it were taken from a different angle. "Arriving at each pixel, the light's coming at a certain angle, and that contains important information," explains Crozier. The new technology also suggests an alternative way to create 3D movies for the big screen. A Seismic Shift in Object Detection | pdollar.

Object detection has undergone a dramatic and fundamental shift. I’m not talking about deep learning here – deep learning is really more about classification and specifically about feature learning. Feature learning has begun to play and will continue to play a critical role in machine vision. Arguably in a few years we’ll have a diversity of approaches for learning rich features hierarchies from large amounts of data; it’s a fascinating topic.

However, as I said, this post is not about deep learning. Rather, perhaps an even more fundamental shift has occurred in object detection: the recent crop of top detection algorithms abandons sliding windows in favor of segmentation in the detection pipeline. Yes, you heard right, segmentation! First some evidence for my claim. The first two are the winning and second place entries on the ImageNet13 detection challenge. So what advantage does region generation give over sliding windows approaches? Observe: Like this: Like Loading... CGI Dude - Very Lifelike. Solving the Tongue-Twisting Problems of Speech Animation. Motion capture is a widely used technique to record the movement of the human body. Indeed, the technique has become ubiquitous in areas such as sports science, where it is used to analyze movement and gait, as well as in movie animation and gaming, where it is used to control computer-generated avatars.

As a result, an entire industry exists for analyzing movement in this way, along with cheap, off-the-shelf equipment and software for capturing and handling the data. “There is a huge community of producers and consumers of motion capture, countless databases and stock animation resources, and a rich ecosystem of proprietary and open-source software tools to manipulate them,” say Ingmar Steiner at Saarland University in Germany and a few pals.

One of the features of the motion capture world is that a de facto standard, known as BioVision Hierarchy or BVH, has emerged for encoding body motion data. All that changes today, thanks to the work of Steiner and co. Garry's Mod. Gameplay[edit] Although Garry's Mod is usually considered to be a full game, it has no game objective and players can use the game's set of tools for any purpose whatsoever, although sometimes when playing on a multiplayer server it may have role-play or other types of game modes. Garry's Mod allows players to manipulate items, furniture and "props" – various objects that players can place in-game. Props can be selected from any installed Source engine game or from a community created collection.

The game features two "guns" – Physics Gun and Tool Gun for manipulating objects. The Physics Gun allows objects to be picked up, adjusted, and frozen in place. The Tool Gun is a multi-purpose tool for performing various tasks, such as combining props, attaching them via ropes, and creating controllable winches and wheels. The Tool Gun is also used to control add-ons created by the community. Another popular Garry's Mod concept is ragdoll posing. Multiplayer[edit] User-created content[edit] Real Time CGI - Lucas Films.

Nvidia Face Works Tech Demo; Renders Realistic Human Faces. Faces. Uncanny valley. In an experiment involving the human lookalike robot Repliee Q2 (pictured above), the uncovered robotic structure underneath Repliee, and the actual human who was the model for Repliee, the human lookalike triggered the highest level of mirror neuron activity.[1] Etymology[edit] The concept was identified by the robotics professor Masahiro Mori as Bukimi no Tani Genshō (不気味の谷現象) in 1970.[5][6] The term "uncanny valley" first appeared in the 1978 book Robots: Fact, Fiction, and Prediction, written by Jasia Reichardt.[7] The hypothesis has been linked to Ernst Jentsch's concept of the "uncanny" identified in a 1906 essay "On the Psychology of the Uncanny".[8][9][10] Jentsch's conception was elaborated by Sigmund Freud in a 1919 essay entitled "The Uncanny" ("Das Unheimliche").[11] Hypothesis[edit] This area of repulsive response aroused by a robot with appearance and motion between a "barely human" and "fully human" entity is called the uncanny valley.

Theoretical basis[edit] Mate selection. Facial Asymmetry. Mapping the Stairs to Visual Excellence. Max Headroom and the Strange World of Pseudo-CGI. I’ve come across people who believe that Max Headroom, the Channel 4 character from the Eighties, was a genuine piece of computer animation. But although he was conceived by the animators Rocky Morton and Annabel Jankel (of Cucumber Films fame) Max himself was portrayed by actor Matt Frewer, placed into latex makeup and a shiny costume and set amidst a range of technological tricks.

Half of the frames from the footage used in Max Headroom were removed in production, resulting in a juddery look to suggest animation shot on twos, and Frewer was bluescreened in front of a basic digital backdrop. The crew even added deliberate faults to the “animation” – such as the stammer which became Max’s trademark – to complete the effect. This process seems somewhat surreal today, in our brave new world of Maya, Xtranormal and Blender.

Another good example of this can be found in the 1981 film Escape from New York. In her book British Animation: The Channel 4 Factor. Photogrammetry. Photogrammetry is an estimative scientific method that aims at recovering the exact positions and motion pathways of designated reference points located on any moving object, on its components and in the immediately adjacent environment. Photogrammetry employs high-speed imaging and the accurate methods of remote sensing in order to detect, measure and record complex 2-D and 3-D motion fields (see also SONAR, RADAR, LiDAR etc.). Photogrammetry feeds the measurements from remote sensing and the results of imagery analysis into computational models in an attempt to successively estimate, with increasing accuracy, the actual, 3-D relative motions within the researched field.

Its applications include satellite tracking of the relative positioning alterations in all Earth environments (e.g. tectonic motions etc), the research on the swimming of fish, of bird or insect flight, other relative motion processes (International Society for Photogrammetry and Remote Sensing). Integration[edit]