Check out Hot Toys MMS422 Beauty and the Beast 1/6th scale Belle Collectible Figure

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Pre-order Hot Toys MMS422 Beauty and the Beast 1/6th scale Belle Collectible Figure from KGHobby HERE

“Look at her! What if she’s the one… the one who will break the spell?”

Disney’s live-action adaptation of its classic animated film Beauty and the Beast has been one of the most anticipated cinematic events of the year and it is the highest grossing live-action movie musical of all time! Based on a timeless fairy tale, the fantasy romance follows dashing village girl Belle as she ventures into an enchanted castle to find her father. There, she agrees to take his place as the captive to the fearsome Beast, who used to be a handsome prince but was cursed to a life as a monster unless he can find true love.

Today, Hot Toys is delighted to present the 1/6th scale collectible figure of Belle from this modern retelling of Beauty and the Beast. Expertly crafted based on Emma Watson’s appearance in the movie, the beautifully realized Belle figure features a newly developed head sculpt, meticulously tailored and intricately multi-layered costume based on her elegant ball gown with various accessories, as well as a specially designed marble-patterned figure stand.

Additionally, the figure comes with a bonus crew of castle’s enchanted household objects, namely Lumière (the candelabra), Cogsworth (the clock), Mrs. Potts and Chip (the teapot and teacup), and Plumette (the feather duster).

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Pre-order Hot Toys MMS422 Beauty and the Beast 1/6th scale Belle Collectible Figure from KGHobby HERE

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Hot Toys MMS422 Beauty and the Beast 1/6th scale Belle Collectible Figure: Newly developed head sculpt with authentic and detailed likeness of Emma Watson as Belle in Disney’s live-action Beauty and the Beast | Highly-accurate facial expression and makeup | Approximately 26 cm tall Body with over 26 points of articulation | Six (6) pieces of interchangeable hands (with a ring on the right hand) including: One (1) pair of relaxed hands, One (1) pair of gesturing hands, One (1) pair of hands for holding the enchanted rose

Costume: slim-fitting, lacy yellow bodice delicately woven from multiple layers of fabric | bell-shaped, yellow satin full skirt | high-heeled dancing shoes | necklace

Bonus Accessories – the Castle’s Enchanted Household Objects: Lumière, the candelabra | Cogsworth, the pendulum clock | Mrs. Potts, the teapot | Chip, the teacup | Plumette, the feather duster (with stand)

Accessories: enchanted rose | enchanted rose (with stand) enclosed in a transparent bell jar | specially designed Beauty and the Beast-themed figure stand covered in patterns modeled after the ballroom’s marble dance floor

Release date: Q4, 2017 – Q1, 2018

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Experimental Nighttime Photography with Nexus and Pixel

Posted by Florian Kainz, Software Engineer, Google Daydream

On a full moon night last year I carried a professional DSLR camera, a heavy lens and a tripod up to a hilltop in the Marin Headlands just north of San Francisco to take a picture of the Golden Gate Bridge and the lights of the city behind it.

A view of the Golden Gate Bridge from the Marin Headlands, taken with a DSLR camera (Canon 1DX, Zeiss Otus 28mm f/1.4 ZE). Click here for the full resolution image.

I thought the photo of the moonlit landscape came out well so I showed it to my (then) teammates in Gcam, a Google Research team that focuses on computational photography – developing algorithms that assist in taking pictures, usually with smartphones and similar small cameras. Seeing my nighttime photo, one of the Gcam team members challenged me to re-take it, but with a phone camera instead of a DSLR. Even though cameras on cellphones have come a long way, I wasn’t sure whether it would be possible to come close to the DSLR shot.

Probably the most successful Gcam project to date is the image processing pipeline that enables the HDR+ mode in the camera app on Nexus and Pixel phones. HDR+ allows you to take photos at low-light levels by rapidly shooting a burst of up to ten short exposures and averaging them them into a single image, reducing blur due to camera shake while collecting enough total light to yield surprisingly good pictures. Of course there are limits to what HDR+ can do. Once it gets dark enough the camera just cannot gather enough light and challenging shots like nighttime landscapes are still beyond reach.

The Challenges
To learn what was possible with a cellphone camera in extremely low-light conditions, I looked to the experimental SeeInTheDark app, written by Marc Levoy and presented at the ICCV 2015 Extreme Imaging Workshop, which can produce pictures with even less light than HDR+. It does this by accumulating more exposures, and merging them under the assumption that the scene is static and any differences between successive exposures must be due to camera motion or sensor noise. The app reduces noise further by dropping image resolution to about 1 MPixel. With SeeInTheDark it is just possible to take pictures, albeit fairly grainy ones, by the light of the full moon.

However, in order to keep motion blur due to camera shake and moving objects in the scene at acceptable levels, both HDR+ and SeeInTheDark must keep the exposure times for individual frames below roughly one tenth of a second. Since the user can’t hold the camera perfectly still for extended periods, it doesn’t make sense to attempt to merge a large number of frames into a single picture. Therefore, HDR+ merges at most ten frames, while SeeInTheDark progressively discounts older frames as new ones are captured. This limits how much light the camera can gather and thus affects the quality of the final pictures at very low light levels.

Of course, if we want to take high-quality pictures of low-light scenes (such as a landscape illuminated only by the moon), increasing the exposure time to more than one second and mounting the phone on a tripod or placing it on some other solid support makes the task a lot easier. Google’s Nexus 6P and Pixel phones support exposure times of 4 and 2 seconds respectively. As long as the scene is static, we should be able to record and merge dozens of frames to produce a single final image, even if shooting those frames takes several minutes.

Even with the use of a tripod, a sharp picture requires the camera’s lens to be focused on the subject, and this can be tricky in scenes with very low light levels. The two autofocus mechanisms employed by cellphone cameras — contrast detection and phase detection — fail when it’s dark enough that the camera’s image sensor returns mostly noise. Fortunately, the interesting parts of outdoor scenes tend to be far enough away that simply setting the focus distance to infinity produces sharp images.

Experiments & Results
Taking all this into account, I wrote a simple Android camera app with manual control over exposure time, ISO and focus distance. When the shutter button is pressed the app waits a few seconds and then records up to 64 frames with the selected settings. The app saves the raw frames captured from the sensor as DNG files, which can later be downloaded onto a PC for processing.

To test my app, I visited the Point Reyes lighthouse on the California coast some thirty miles northwest of San Francisco on a full moon night. I pointed a Nexus 6P phone at the building and shot a burst of 32 four-second frames at ISO 1600. After covering the camera lens with opaque adhesive tape I shot an additional 32 black frames. Back at the office I loaded the raw files into Photoshop. The individual frames were very grainy, as one would expect given the tiny sensor in a cellphone camera, but computing the mean of all 32 frames cleaned up most of the grain, and subtracting the mean of the 32 black frames removed faint grid-like patterns caused by local variations in the sensor’s black level. The resulting image, shown below, looks surprisingly good.

Point Reyes lighthouse at night, photographed with Google Nexus 6P (full resolution image here).

The lantern in the lighthouse is overexposed, but the rest of the scene is sharp, not too grainy, and has pleasing, natural looking colors. For comparison, a hand-held HDR+ shot of the same scene looks like this:

Point Reyes Lighthouse at night, hand-held HDR+ shot (full resolution image here). The inset rectangle has been brightened in Photoshop to roughly match the previous picture.

Satisfied with these results, I wanted to see if I could capture a nighttime landscape as well as the stars in the clear sky above it, all in one picture. When I took the photo of the lighthouse a thin layer of clouds conspired with the bright moonlight to make the stars nearly invisible, but on a clear night a two or four second exposure can easily capture the brighter stars. The stars are not stationary, though; they appear to rotate around the celestial poles, completing a full turn every 24 hours. The motion is slow enough to be invisible in exposures of only a few seconds, but over the minutes it takes to record a few dozen frames the stars move enough to turn into streaks when the frames are merged. Here is an example:

The North Star above Mount Burdell, single 2-second exposure. (full resolution image here).
Mean of 32 2-second exposures (full resolution image here).

Seeing streaks instead of pinpoint stars in the sky can be avoided by shifting and rotating the original frames such that the stars align. Merging the aligned frames produces an image with a clean-looking sky, and many faint stars that were hidden by noise in the individual frames become visible. Of course, the ground is now motion-blurred as if the camera had followed the rotation of the sky.

Mean of 32 2-second exposures, stars aligned (full resolution image here).

We now have two images; one where the ground is sharp, and one where the sky is sharp, and we can combine them into a single picture that is sharp everywhere. In Photoshop the easiest way to do that is with a hand-painted layer mask. After adjusting brightness and colors to taste, slight cropping, and removing an ugly “No Trespassing” sign we get a presentable picture:

The North Star above Mount Burdell, shot with Google Pixel, final image (full resolution image here).

Using Even Less Light
The pictures I’ve shown so far were shot on nights with a full moon, when it was bright enough that one could easily walk outside without a lantern or a flashlight. I wanted to find out if it was possible to take cellphone photos in even less light. Using a Pixel phone, I tried a scene illuminated by a three-quarter moon low in the sky, and another one with no moon at all. Anticipating more noise in the individual exposures, I shot 64-frame bursts. The processed final images still look fine:

Wrecked fishing boat in Inverness and the Big Dipper, 64 2-second exposures, shot with Google Pixel (full resolution image here).
Stars above Pierce Point Ranch, 64 2-second exposures, shot with Google Pixel (full resolution image here).

In the second image the distant lights of the cities around the San Francisco Bay caused the sky near the horizon to glow, but without moonlight the night was still dark enough to make the Milky Way visible. The picture looks noticeably grainier than my earlier moonlight shots, but it’s not too bad.

Pushing the Limits
How far can we go? Can we take a cellphone photo with only starlight – no moon, no artificial light sources nearby, and no background glow from a distant city?

To test this I drove to a point on the California coast a little north of the mouth of the Russian River, where nights can get really dark, and pointed my Pixel phone at the summer sky above the ocean. Combining 64 two-second exposures taken at ISO 12800, and 64 corresponding black black frames did produce a recognizable image of the Milky Way. The constellations Scorpius and Sagittarius are clearly visible, and squinting hard enough one can just barely make out the horizon and one or two rocks in the ocean, but overall, this is not a picture you’d want to print out and frame. Still, this may be the lowest-light cellphone photo ever taken.

Only starlight, shot with Google Pixel (full resolution image here).

Here we are approaching the limits of what the Pixel camera can do. The camera cannot handle exposure times longer than two seconds. If this restriction was removed we could expose individual frames for eight to ten seconds, and the stars still would not show noticeable motion blur. With longer exposures we could lower the ISO setting, which would significantly reduce noise in the individual frames, and we would get a correspondingly cleaner and more detailed final picture.

Getting back to the original challenge – using a cellphone to reproduce a night-time DSLR shot of the Golden Gate – I did that. Here is what I got:

Golden Gate Bridge at night, shot with Google Nexus 6P (full resolution image here).
The Moon above San Francisco, shot with Google Nexus 6P (full resolution image here).

At 9 to 10 MPixels the resolution of these pictures is not as high as what a DSLR camera might produce, but otherwise image quality is surprisingly good: the photos are sharp all the way into the corners, there is not much visible noise, the captured dynamic range is sufficient to avoid saturating all but the brightest highlights, and the colors are pleasing.

Trying to find out if phone cameras might be suitable for outdoor nighttime photography was a fun experiment, and clearly the result is yes, they are. However, arriving at the final images required a lot of careful post-processing on a desktop computer, and the procedure is too cumbersome for all but the most dedicated cellphone photographers. However, with the right software a phone should be able to process the images internally, and if steps such as painting layer masks by hand can be eliminated, it might be possible to do point-and-shoot photography in very low light conditions. Almost – the cellphone would still have to rest on the ground or be mounted on a tripod.

Here’s a Google Photos album with more examples of photos that were created with the technique described above.

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Hot Toys Spider-Man: Homecoming 1/6th scale Iron Man Mark XLVII Power Pose Collectible

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Taking the role as young Spider-Man Peter Parker’s mentor, Tony Stark helps Parker to balance the normal routine life as a high school student and the crime fighting superhero life after the events of Marvel’s Captain American: Civil War.

Celebrating the global release of the long awaited Marvel’s Spider-Man: Homecoming arriving this summer, Hot Toys is thrilled to present to you the Power Pose Series 1/6th scale collectible figure of Tony Stark’s new Iron Man armor in Spider-Man: Homecoming – the Mark XLVII as a Movie Promo Edition item only available in selected markets!

In reference to the appearance of Iron Man’s new armor in the upcoming Marvel Studios blockbuster, the Mark XLVII (Movie Promo Edition) Power Pose collectible figure is sophisticatedly crafted and designed with movie-accurate quality.

It features a meticulously sculpted sleek, streamline armor and trapezoid-shaped indent around the arc reactor. Equipped with 28 LED light-up points throughout the body, it is also given the iconic red and gold color featured mostly on the helmet and shoulder, as well as the distinctive silver and dark silver colored painting on the mid-section of the armor. This Power Pose collectible figure also comes with articulated head and arms, interchangeable fists and repulsor palms, and a figure base.

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Hot Toys PPS004 Spider-Man: Homecoming 1/6th scale Iron Man Mark XLVII Power Pose Collectible features: Authentic and detailed likeness of Mark XLVII in Spider-Man: Homecoming | Approximately 35 cm tall | Metallic red, gold, silver and dark silver colored painting on the sleek and streamline Mark XLVI armor design | 28 LED light-up points throughout parts of the armor (white light, battery operated) | Articulated head and arms | Two (2) pairs of interchangeable hands including: One (1) pair of fists, One (1) pair of repulsor firing palms (white light, battery operated) | Figure stand with transparent pole, Mark XLVII nameplate and Spider-Man: Homecoming logo

Release date: End of Q2 – Q3, 2017

It must be TOUGH being a Hot Toys Iron Man collector. If you are going to collect every armor Hot Toys ever produced, then these Power Pose Iron Man figures must also be included. That’s a lot of Iron Man figures! Anybody ever bothered to count them all? Not just going by the numbers but let’s not forget the variants and special editions.

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Research at Google and ICLR 2017

Posted by Ian Goodfellow, Staff Research Scientist, Google Brain Team

This week, Toulon, France hosts the 5th International Conference on Learning Representations (ICLR 2017), a conference focused on how one can learn meaningful and useful representations of data for Machine Learning. ICLR includes conference and workshop tracks, with invited talks along with oral and poster presentations of some of the latest research on deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.

At the forefront of innovation in cutting-edge technology in Neural Networks and Deep Learning, Google focuses on both theory and application, developing learning approaches to understand and generalize. As Platinum Sponsor of ICLR 2017, Google will have a strong presence with over 50 researchers attending (many from the Google Brain team and Google Research Europe), contributing to and learning from the broader academic research community by presenting papers and posters, in addition to participating on organizing committees and in workshops.

If you are attending ICLR 2017, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at ICLR 2017 in the list below (Googlers highlighted in blue).

Area Chairs include:
George Dahl, Slav Petrov, Vikas Sindhwani

Program Chairs include:
Hugo Larochelle, Tara Sainath

Contributed Talks
Understanding Deep Learning Requires Rethinking Generalization (Best Paper Award)
Chiyuan Zhang*, Samy Bengio, Moritz Hardt, Benjamin Recht*, Oriol Vinyals

Semi-Supervised Knowledge Transfer for Deep Learning from Private Training Data (Best Paper Award)
Nicolas Papernot*, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal
Talwar

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang (Shane) Gu*, Timothy Lillicrap, Zoubin Ghahramani, Richard E.
Turner, Sergey Levine

Neural Architecture Search with Reinforcement Learning
Barret Zoph, Quoc Le

Posters
Adversarial Machine Learning at Scale
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio

Capacity and Trainability in Recurrent Neural Networks
Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo

Improving Policy Gradient by Exploring Under-Appreciated Rewards
Ofir Nachum, Mohammad Norouzi, Dale Schuurmans

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc LeGeoffrey Hinton, Jeff Dean

Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole*, David Pfau, Jascha Sohl-Dickstein

Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang (Shane) Gu*, Ben Poole*

Decomposing Motion and Content for Natural Video Sequence Prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee

Density Estimation Using Real NVP
Laurent Dinh*, Jascha Sohl-Dickstein, Samy Bengio

Latent Sequence Decompositions
William Chan*, Yu Zhang*, Quoc Le, Navdeep Jaitly*

Learning a Natural Language Interface with Neural Programmer
Arvind Neelakantan*, Quoc V. Le, Martín Abadi, Andrew McCallum*, Dario
Amodei*

Deep Information Propagation
Samuel Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein

Identity Matters in Deep Learning
Moritz Hardt, Tengyu Ma

A Learned Representation For Artistic Style
Vincent Dumoulin*, Jonathon Shlens, Manjunath Kudlur

Adversarial Training Methods for Semi-Supervised Text Classification
Takeru Miyato, Andrew M. Dai, Ian Goodfellow

HyperNetworks
David Ha, Andrew Dai, Quoc V. Le

Learning to Remember Rare Events
Lukasz Kaiser, Ofir Nachum, Aurko Roy*, Samy Bengio

Deep Learning with Dynamic Computation Graphs
Moshe Looks, Marcello Herreshof, DeLesley Hutchins, Peter Norvig

HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Cezary Kaliszyk, François Chollet, Christian Szegedy

Hyperband: Bandit-based Configuration Evaluation for Hyperparameter Optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

Workshop Track Abstracts
Particle Value Functions
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

Neural Combinatorial Optimization with Reinforcement Learning
Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio

Short and Deep: Sketching and Neural Networks
Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar

Explaining the Learning Dynamics of Direct Feedback Alignment
Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein

Training a Subsampling Mechanism in Expectation
Colin Raffel, Dieterich Lawson

Tuning Recurrent Neural Networks with Reinforcement Learning
Natasha Jaques*, Shixiang (Shane) Gu*, Richard E. Turner, Douglas Eck

REBAR: Low-Variance, Unbiased Gradient Estimates for Discrete Latent Variable Models
George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein

Adversarial Examples in the Physical World
Alexey Kurakin, Ian Goodfellow, Samy Bengio

Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, Geoffrey Hinton

Unsupervised Perceptual Rewards for Imitation Learning
Pierre Sermanet, Kelvin Xu, Sergey Levine

Changing Model Behavior at Test-time Using Reinforcement Learning
Augustus Odena, Dieterich Lawson, Christopher Olah

* Work performed while at Google
† Work performed while at OpenAI

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April 2017 Haul #2: Hot Toys 1/6th scale Black Panther, Shoretrooper and Bandai Yoda model kit

This is my second haul this month (check out my earlier haul HERE) and I got the Hot Toys MMS363 Captain America: Civil War 1/6th scale Black Panther Collectible Figure, along with Hot Toys MMS389 Rogue One: A Star Wars Story 1/6th scale Shoretrooper Collectible Figure plus the Bandai Star Wars 1/6th scale Yoda model kit. Black Panther took a while to arrive but I was caught off guard by the Shoretrooper. Bandai Yoda was just the icing on the cake, unplanned but certainly interesting. More on that later.

In the 2016 superhero film Captain America: Civil War which was a little like the continuation from the Avengers: Age of Ultron because almost everybody who was in the film was in this one as well, we were introduced to a new Marvel character, the Black Panther. I think it was great casting Chadwick Boseman as T’Challa / Black Panther, the prince of the African nation of Wakanda allied with Stark. I’ve known the character since his comic book days and it was exciting to see the character being realized on the big screen.

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The box packaging design is a nod to the Marvel civil war comic book covers which were divided into half, with the art shown on the top half and just a white box below highlighting the words “Civil War”.  It’s a nice concept but I would have preferred a full bleed image of the character instead.

Hot Toys MMS363 Captain America: Civil War 1/6th scale Black Panther Collectible Figure has absolutely not much accessories except for a couple of interchangeable gloved hands and the standard display stand. I don’t pose my figures with the stand as I much prefer them to be free standing and interacting with each other. And you can only have the figure with a pair of hands at any one time so the other hands stay in the box. What else is there? Nada, nought, zip, zilch, zero, bupkis – many words but still one meaning and that’s coming up empty. It would have been really nice on Hot Toys part to include a 1/6th scale Chadwick Boseman head sculpt for the unmasked look.

So why get this figure? The costume! I love the design of it, that it’s not just black like in the comic books but there are fine details everywhere. According to the costume designers, the Black Panther costume is a combination of a practical costume and visual effects, featuring a vibranium mesh weave similar to chainmail. Costume designer Judianna Makovsky called the Black Panther costume “difficult” since “you needed sort of a feline body, but it’s hard and practical at the same time. You needed a feeling of some sort of ethnicity in there, but of a world [Wakanda] we weren’t really creating yet, so you didn’t want to go too far and say too much about that world.” And you get to see this costume replicated in highly accurate 1/6th scale by Hot Toys. Check out the preview pics HERE.

Rogue One: A Star Wars Story is the first installment of the Star Wars Anthology series, set immediately before the events of the original Star Wars film. The cast includes Felicity Jones, Diego Luna, Riz Ahmed, Ben Mendelsohn, Donnie Yen, Mads Mikkelsen, Alan Tudyk, Jiang Wen and Forest Whitaker. Rogue One follows a group of rebels on a mission to steal the plans for the Death Star, the Galactic Empire’s superweapon. The Star Wars standalone film introduced us to some new troopers, alongside the familiar ones we are used to seeing, like the Stormtroopers. Among the new troopers were the Coastal Defender Stormtroopers, more commonly known as Shoretroopers, a specialized variant of the Galactic Empire’s stormtroopers trained and equipped for combat in tropical environments.

Stationed at the top secret Imperial security complex on the tropical planet Scarif, shoretroopers patrolled the beaches and bunkers of the facility. Shoretroopers operate, effectively, as Sergeants which allow them to command small squads of regular stormtroopers. There were at least three distinct ranks of shoretroopers, all with unique armor markings. Regular shoretroopers featured sand colored armor, with a red band around their right arm and a white stripe around their left shoulder guard. Squad leaders were identified by a sand blue stripe that went along the top of their chest plates and onto the top of their shoulder guards. Squad leaders also sported a kama attached to their belt.

Shoretrooper captains had almost all of their chest plates blue, along with a small plate of armor that was on their left side. Their right shoulder guard was sand colored, but their left shoulder guard was all sand blue, save for the white stripe. Further down the same arm, there were stripes of sand blue and yellow.

So this Hot Toys MMS389 Rogue One: A Star Wars Story 1/6th scale Shoretrooper Collectible Figure is actually a Shoretrooper Captain. You can see the preview pics HERE

And then there’s this Bandai Star Wars 1/6th scale Yoda Model Kit. Having gotten the Bandai 1/6th scale Star Wars Stormtrooper Model Kit which turned out to be quite impressive with full articulation (see the full action figure review posted on my toy blog HERE), I decided to try my hand at this 1/6th scale Yoda model kit.

There are two sizes included, the 1/6th scale version and a 1/12th scale version. The 1/12th scale Yoda is a statue when assembled but the 1/6th scale version does have articulation as well. More on plastic model kit Yoda in the upcoming posts.

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Toys-Era TE017 1/6th scale The Laughter 12-inch collectible figure is The Joker from Gotham

Pre-order Toys Era TE017 1/6th scale The Laughter Collectible Figure from KGHobby HERE

Gotham is an American crime drama television series developed by Bruno Heller, based on characters published by DC Comics and appearing in the Batman franchise, primarily those of James Gordon and Bruce Wayne. The series stars Ben McKenzie as the young Gordon, while Heller executive-produces, along with Danny Cannon, who also directed the pilot.

Originally the series would have related only Gordon’s early days on the Gotham City Police Department, but the series subsequently included the Wayne character and the origin stories of several Batman villains, including the Penguin, the Riddler, Catwoman, Poison Ivy, Two-Face, the Scarecrow, the Joker, Mr. Freeze, and Hugo Strange.

A character based on the Joker, named Jerome Valeska (Cameron Monaghan), is featured in Gotham. Monaghan stated that he drew inspiration from previous interpretations of the Joker for his performance, particularly that of Mark Hamill, adding “I think he’s the Joker in that he represents the idea, the greater concept.” The mythology of the Joker is explored further during season three, and Jerome himself returns in the second half of the season bearing a physical resemblance to The New 52 incarnation of the Joker; Jerome’s face was surgically removed before his resurrection, forcing him to staple it back in place.

Pre-order Toys Era TE017 1/6th scale The Laughter Collectible Figure from KGHobby HERE

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Toys-Era TE017 1/6th scale The Laughter 12-inch collectible figure features: Head sculpt with rolling eyeballs and detailed face skin texture, scar and stitch marks | approximately 32cm tall Body with over 30 points of articulations | Seven (7) pieces of interchangeable gloved hands

Costume: white shirt, Red vest with Yellow flower clip, Black bow tie, White pants, black leather boots

Accessories: Pistol, knife, Loudspeaker, Figure stand

Release date: Q2 2017

Pre-order Toys Era TE017 1/6th scale The Laughter Collectible Figure from KGHobby HERE

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