Cross-posted: http://newmeism.blogspot.com.au/2017/02/monthly-wrap-up-february-2017.htmlFollow over here for regular updates with this sort of stuff: http://newmeism.blogspot.com.au/—Reducing waste. Making changes. Etc. It all comes down to… Continua a leggere
Scale 1:12Foto: Minichamps Continua a leggere
Posted by Maggie Johnson, Director of Education and University Relations, Google
We’ve just completed another round of the Google Research Awards, our annual open call for proposals on computer science and related topics including machine learning, machine perception, natural language processing, and security. Our grants cover tuition for a graduate student and provide both faculty and students the opportunity to work directly with Google researchers and engineers.
This round we received 876 proposals covering 44 countries and over 300 universities. After expert reviews and committee discussions, we decided to fund 143 projects. Here are a few observations from this round:
- The subject areas that received the most support were machine learning, machine perception, networking and systems.
- Proposals related to Machine learning represented 20% of the total submissions received, up from 12% in 2015.
- Proportionally, proposals from Europe had a 4% higher acceptance rate, attributed to our increased research presence in Zürich.
Congratulations to the well-deserving recipients of this round’s awards. If you are interested in applying for the next round (deadline is September 30th), please visit our website for more information.
Posted by Kester Tong, David Soergel, and Gus Katsiapis, Software Engineers
When applying machine learning to real world datasets, a lot of effort is required to preprocess data into a format suitable for standard machine learning models, such as neural networks. This preprocessing takes a variety of forms, from converting between formats, to tokenizing and stemming text and forming vocabularies, to performing a variety of numerical operations such as normalization.
Today we are announcing tf.Transform, a library for TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph. Users define a pipeline by composing modular Python functions, which tf.Transform then executes with Apache Beam, a framework for large-scale, efficient, distributed data processing. Apache Beam pipelines can be run on Google Cloud Dataflow with planned support for running with other frameworks. The TensorFlow graph exported by tf.Transform enables the preprocessing steps to be replicated when the trained model is used to make predictions, such as when serving the model with Tensorflow Serving.
A common problem encountered when running machine learning models in production is “training-serving skew”, where the data seen at serving time differs in some way from the data used to train the model, leading to reduced prediction quality. tf.Transform ensures that no skew can arise during preprocessing, by guaranteeing that the serving-time transformations are exactly the same as those performed at training time, in contrast to when training-time and serving-time preprocessing are implemented separately in two different environments (e.g., Apache Beam and TensorFlow, respectively).
In addition to facilitating preprocessing, tf.Transform allows users to compute summary statistics for their datasets. Understanding the data is very important in every machine learning project, as subtle errors can arise from making wrong assumptions about what the underlying data look like. By making the computation of summary statistics easy and efficient, tf.Transform allows users to check their assumptions about both raw and preprocessed data.
We’re excited to be releasing this latest addition to the TensorFlow ecosystem, and we hope users will find it useful for preprocessing and understanding their data.
We wish to thank the following members of the tf.Transform team for their contributions to this project: Clemens Mewald, Robert Bradshaw, Rajiv Bharadwaja, Elmer Garduno, Afshin Rostamizadeh, Neoklis Polyzotis, Abhi Rao, Joe Toth, Neda Mirian, Dinesh Kulkarni, Robbie Haertel, Cyril Bortolato and Slaven Bilac. We also wish to thank the TensorFlow, TensorFlow Serving and Cloud Dataflow teams for their support.
Posted by Vivek Kwatra, Research Scientist and Christian Frueh, Avneesh Sud, Software Engineers
Virtual Reality (VR) enables remarkably immersive experiences, offering new ways to view the world and the ability to explore novel environments, both real and imaginary. However, compared to physical reality, sharing these experiences with others can be difficult, as VR headsets make it challenging to create a complete picture of the people participating in the experience.
Some of this disconnect is alleviated by Mixed Reality (MR), a related medium that shares the virtual context of a VR user in a two dimensional video format allowing other viewers to get a feel for the user’s virtual experience. Even though MR facilitates sharing, the headset continues to block facial expressions and eye gaze, presenting a significant hurdle to a fully engaging experience and complete view of the person in VR.
Google Machine Perception researchers, in collaboration with Daydream Labs and YouTube Spaces, have been working on solutions to address this problem wherein we reveal the user’s face by virtually “removing” the headset and create a realistic see-through effect.
Our approach uses a combination of 3D vision, machine learning and graphics techniques, and is best explained in the context of enhancing Mixed Reality video (also discussed in the Google-VR blog). It consists of three main components:
Dynamic face model capture
The core idea behind our technique is to use a 3D model of the user’s face as a proxy for the hidden face. This proxy is used to synthesize the face in the MR video, thereby creating an impression of the headset being removed. First, we capture a personalized 3D face model for the user with what we call gaze-dependent dynamic appearance. This initial calibration step requires the user to sit in front of a color+depth camera and a monitor, and then track a marker on the monitor with their eyes. We use this one-time calibration procedure — which typically takes less than a minute — to acquire a 3D face model of the user, and learn a database that maps appearance images (or textures) to different eye-gaze directions and blinks. This gaze database (i.e. the face model with textures indexed by eye-gaze) allows us to dynamically change the appearance of the face during synthesis and generate any desired eye-gaze, thus making the synthesized face look natural and alive
Calibration and Alignment
Creating a Mixed Reality video requires a specialized setup consisting of an external camera, calibrated and time-synced with the headset. The camera captures a video stream of the VR user in front of a green screen and then composites a cutout of the user with the virtual world to create the final MR video. An important step here is to accurately estimate the calibration (the fixed 3D transformation) between the camera and headset coordinate systems. These calibration techniques typically involve significant manual intervention and are done in multiple steps. We simplify the process by adding a physical marker to the front of the headset and tracking it visually in 3D, which allows us to optimize for the calibration parameters automatically from the VR session.
For headset “removal”, we need to align the 3D face model with the visible portion of the face in the camera stream, so that they would blend seamlessly with each other. A reasonable proxy to this alignment is to place the face model just behind the headset. The calibration described above, coupled with VR headset tracking, provides sufficient information to determine this placement, allowing us to modify the camera stream by rendering the virtual face into it.
Compositing and Rendering
Having tackled the alignment, the last step involves producing a suitable rendering of the 3D face model, consistent with the content in the camera stream. We are able to reproduce the true eye-gaze of the user by combining our dynamic gaze database with an HTC Vive headset that has been modified by SMI to incorporate eye-tracking technology. Images from these eye trackers lack sufficient detail to directly reproduce the occluded face region, but are well suited to provide fine-grained gaze information. Using the live gaze data from the tracker, we synthesize a face proxy that accurately represents the user’s attention and blinks. At run-time, the gaze database, captured in the preprocessing step, is searched for the most appropriate face image corresponding to the query gaze state, while also respecting aesthetic considerations such as temporal smoothness. Additionally, to account for lighting changes between gaze database acquisition and run-time, we apply color correction and feathering, such that the synthesized face region matches with the rest of the face.
Humans are highly sensitive to artifacts on faces, and even small imperfections in synthesis of the occluded face can feel unnatural and distracting, a phenomenon known as the “uncanny valley.” To mitigate this problem, we do not remove the headset completely, instead we have chosen a user experience that conveys a ‘scuba mask effect’ by compositing the color corrected face proxy with a translucent headset. Reminding the viewer of the presence of the headset helps us avoid the uncanny valley, and also makes our algorithm robust to small errors in alignment and color correction.
This modified camera stream, displaying a see-through headset, with the user’s face revealed and their true eye-gaze recreated, is subsequently merged with the virtual environment to create the final MR video.
Results and Extensions
We have used our headset removal technology to enhance Mixed Reality, allowing the medium to not only convey a VR user’s interaction with the virtual environment but also show their face in a natural and convincing fashion. The example below demonstrates our tech applied to an artist using Google Tilt Brush in a virtual environment:
While we have shown the potential of our technology, its applications extend beyond Mixed Reality. Headset removal is poised to enhance communication and social interaction in VR itself with diverse applications like VR video conference meetings, multiplayer VR gaming, and exploration with friends and family. Going from an utterly blank headset to being able to see, with photographic realism, the faces of fellow VR users promises to be a significant transition in the VR world, and we are excited to be a part of it.
Often corrosion is seen as something that happens, when a material is exposed to the elements.Corrosion however, happens very often much earlier, namely on the construction sites, where the materials are stored inappropriately and without thought for w… Continua a leggere
The Tet Offensive was one of the largest military campaigns of the Vietnam War, launched on January 30, 1968, by forces of the Viet Cong and North Vietnamese People’s Army of Vietnam against the forces of the South Vietnamese Army of the Republic of Vietnam, the United States Armed Forces, and their allies. It was a campaign of surprise attacks against military and civilian command and control centers throughout South Vietnam. The name of the offensive comes from the Tết holiday, the Vietnamese New Year, when the first major attacks took place.
Though initial attacks stunned both the US and South Vietnamese armies, causing them to temporarily lose control of several cities, they quickly regrouped, beat back the attacks, and inflicted heavy casualties on North Vietnamese forces. In the 1987 British-American war film “Full Metal Jacket” directed and produced by Stanley Kubrick, the storyline follows a platoon of U.S. Marines through their training and the experiences of two of the platoon’s Marines in the Tet Offensive during the Vietnam War. Adam Baldwin stars as “Animal Mother”, an M60 machine gunner. The M60 later served in the Vietnam War as a squad automatic weapon with many U.S. units. Every soldier in the rifle squad would carry an additional 200 linked rounds of ammunition for the M60, a spare barrel, or both. During the Vietnam War, the M60 received the nickname “The Pig” due to its bulky size.
Dam Toys No.78038 History Series 1/6th scale Vietnam War U.S. Marine (Tet Offensive, 1968) 12-inch figure Parts list: Head sculpt, Dam 3.0 Action Body (With Muscle Arm), Hands For Holding Weapon X5, M1 Helmet, Mitchell Pattern Helmet Cover, T-Shirt, 2Nd Pattern OG 107 Pants, Trouser Belt, USMC M1955 Flack Vest, 3Rd Pattern Jungle Boots, M1956 Belt, USMC Jungle First Aid Kit Pouch, M1911A1 Pistol, M1911A1 .45 Mag X2, M1911A1 .45 Mag Pouch, M1916 Leather Pistol Holster, USMC Ka-Bar Knife, Leather Sheath, 1 Quart Canteen X3, M1944 Canteens Pouch X2, M17A1 Gas Mask, Gas Mask Bag, ARVN Rucksack, Nylon Poncho, M18 Smoke Grenade X2, INCHN TH Grenade, M26 Grenade X3, Carabineer, Watch, Insect Repellent, Toothbrush, Weapons Oil, Cigarette Pack X2, Smoking Cigarette, M60 Machine Gun, M60 7.62mm Linked Ammo (Metal), M60 7.62Mm Linked Ammo Belt X3, M60 Cloth, Ammo Bandoleer With Ammo
“To the Batmobile!”
This was part of my haul from December 2016 posted on my toy blog HERE
The Batmobile is a state of the art all-terrain, self-powered, armored fighting motor vehicle used for vehicular hot pursuit, prisoner transportation, anti-tank warfare, riot control, and as a mobile crime lab. Kept in the Batcave, which it accesses through a hidden entrance, the heavily armoured, gadget-laden vehicle is used by Batman in his crime-fighting activities.
The Batmobile made its first appearance in Detective Comics #27 (May, 1939). Then a red sedan, it was simply referred to as “his car”. Soon it began featuring an increasingly prominent bat motif, typically including distinctive wing-shaped tailfins. Armored in the early stages of Batman’s career, it has been customized over time into a sleek armoured / supercar-hybrid, and is the most technologically advanced crime-fighting asset within Batman’s arsenal. Depictions of the vehicle has evolved along with the character, with each incarnation reflecting evolving car technologies. It has appeared in every Batman iteration—from comic books and television to films and video games—and has since gone on to be a part of pop culture.
The Batman v Superman: Dawn of Justice Batmobile is said to be a combined inspiration from both the sleek, streamlined design of classic Batmobiles and the high-suspension, military build from the more recent Tumbler from The Dark Knight Trilogy. Designed by production designer Patrick Tatopoulos and Dennis McCarthy, the Batmobile is about 20 feet long and 12 feet wide. Unlike previous Batmobiles, it has a gatling gun sitting on the front and the back tires are shaved down tractor tires. The Batmobile elevates itself for scenes depicting it going into battle or when performing jumps, and lowers to the ground when cruising through the streets.
Jada Toys released this 1:24 die-cast model kit of the Batmobile from the 2016 movie Batman v Superman: Dawn of Justice. Assembly was pretty straight-forward and hassle-free. It isn’t anything like the type of model kits released by Bandai where one really has to put every bit and piece together. Check out my recent toy blog post / review of the Bandai 1/6th scale Star Wars Stormtrooper Model Kit HERE. Now that’s a model kit.
In the upcoming film Justice League, Batman will have a new Batmobile called the Nightcrawler, which was designed by his father.