#for real this time instead of the 3d equivalent of a sketch

[ssspringroll]

why do i keep designing plantsim hairs that cover the eyes/most of the face. this is. this is gonna be the 3rd one.

[alexanderessexmediaroles]

Day 16 - Model Making in 3D

Similar to the model making post I made yesterday, I wanted to take a quick interest towards 3D modelling for the research project as I am fascinated to what goes on and much it compares to traditional model making in stop motion. 3D Modelling is similar to stop motion modelling but instead of making multiple models just in case one breaks, you only need to create one model for the scene as that model can be reused over and over again. Because of this, model makers can make the model as perfect and detailed as possible due to not needing to interchange the model for scenes compared to stop-motion. However that’s not to say modelers have complete reign to what they like to model as they still need to communicate to the director, animators and riggers their development with the model otherwise a rigger might find some difficulty trying to make the skeleton for it, animators might struggle if there’s too many loose or unnecessary bit’s dangling off the character that would also need to be animated as well keeping in line with the directors vision. 

Whilst working as a 3D model maker, there’s a variety of software that can is used within the role which are; Maya, 3D’s Max, ZBrush, Blender, RenderMan, Cinema4D and Sketch Up. These all serve different process when it comes towards modelling as for the ones that I am familiar with (that being Maya and ZBrush), Maya helps to import the model and help refine and tweak the model ready for the rigging artist and ZBrush is often used to help create the puppet  from scratch. Maya can be used as a sculpting tool instead of ZBrush and is definitely the more cost effective way, however ZBrush provides so much more detail and refinement compared to Maya as you have a lot more options and possibilities compared to Maya as well as being so much easier to use. However, sometimes model makers will have to also retopology their models in Maya so that they have something ready for the riggers to work with. With the other types of software like Blender and Cinema4D, I’m definitely less familiar with them although from my research, Maya tends to be the most introductory software out of them all as if you know how to use Maya, then those skills are very easily transferred over to the other software as it normally takes about a week or two to adjust to the new software.

As for the modelling itself, you fundamentally start off from a block or sphere and you just keep on adding to it like you would normally do with a piece of sculpting clay just done digitally. One of my favorite pieces of media that really displays model-making real well is the video game series ‘Overwatch’  as the character designs that the model makers bring to life through using ZBrush is truly mesmerising to watch as well as feeling really inspired to do go with the 3D/CG route due to the amount of opportunity to work in either film, games or advertising through modelling, animating or rigging. 

Overwatch Mercy - Speed Sculpt - FREE MODEL DOWNLOAD

Official ZBrush Summit 2016 Presentation - Blizzard Entertainment

One last bit to mention with 3D Model Making, is that I looked at LinkedIn to what some studios would require a 3D modeler to have if they wanted to work in the industry. One spot I saw was a bit on the high end was at a games company called ‘2K Games’ where they’re requirements were much higher than expected as they wanted at least 5 years of experience in 3D Modelling in Maya or 3DSMax, experience with ZBrush, experience with some other software’s like substance painter/designer that I never knew about as well as the usual stuff of being a team player and having good management skills. This honestly surprised me when I first saw this as I didn’t expect that there would be this many pieces of software to become just a model maker as it was a really eye-opening moment for me. Comparing this to a low-end modelling 3D job, at most they would want you to learn Maya or an equivalent software.

Full List: 

5+ years game experience in 3D modelling in Maya or 3DSMax

Experience working with Zbrush or Mudbox

Experience with Substance Painter/Designer & PBR workflows

Expert knowledge of Photoshop

At least 1 shipped AAA title on current gen consoles

Strong UV unwrapping and mapping skills

Ability to effectively communicate and collaborate across multiple locations and time zones in a clear and efficient manner

A team player with strong communication skills, a positive attitude and a high degree of self-motivation and initiative

Strong technical skills and understanding of modern game art pipelines and methodologies

Good time management skills with the ability to prioritise tasks & communicate clearly & constructively with production

Overall, I’m really happy that I did look into 3D modelling as another potential role as it might be something I consider if I plan on going more the 3D route than Stop Motion as I feel there’s a whole lot more work opportunity within the medium. In addition, model-making in 3D is very different to stop motion career as the job as a 3D modeler is something you stick with rather than having multiple different roles stacked on top of each other. However having said that, I still like that idea of multi-tasking like that which is why i’m still unsure between the roles.

What is 3D Modeling & What’s It Used For?

https://conceptartempire.com/what-is-3d-modeling/

[dorcasrempel]

System trains driverless cars in simulation before they hit the road

A simulation system invented at MIT to train driverless cars creates a photorealistic world with infinite steering possibilities, helping the cars learn to navigate a host of worse-case scenarios before cruising down real streets.  

Control systems, or “controllers,” for autonomous vehicles largely rely on real-world datasets of driving trajectories from human drivers. From these data, they learn how to emulate safe steering controls in a variety of situations. But real-world data from hazardous “edge cases,” such as nearly crashing or being forced off the road or into other lanes, are — fortunately — rare.

Some computer programs, called “simulation engines,” aim to imitate these situations by rendering detailed virtual roads to help train the controllers to recover. But the learned control from simulation has never been shown to transfer to reality on a full-scale vehicle.

The MIT researchers tackle the problem with their photorealistic simulator, called Virtual Image Synthesis and Transformation for Autonomy (VISTA). It uses only a small dataset, captured by humans driving on a road, to synthesize a practically infinite number of new viewpoints from trajectories that the vehicle could take in the real world. The controller is rewarded for the distance it travels without crashing, so it must learn by itself how to reach a destination safely. In doing so, the vehicle learns to safely navigate any situation it encounters, including regaining control after swerving between lanes or recovering from near-crashes.  

In tests, a controller trained within the VISTA simulator safely was able to be safely deployed onto a full-scale driverless car and to navigate through previously unseen streets. In positioning the car at off-road orientations that mimicked various near-crash situations, the controller was also able to successfully recover the car back into a safe driving trajectory within a few seconds. A paper describing the system has been published in IEEE Robotics and Automation Letters and will be presented at the upcoming ICRA conference in May.

“It’s tough to collect data in these edge cases that humans don’t experience on the road,” says first author Alexander Amini, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “In our simulation, however, control systems can experience those situations, learn for themselves to recover from them, and remain robust when deployed onto vehicles in the real world.”

The work was done in collaboration with the Toyota Research Institute. Joining Amini on the paper are Igor Gilitschenski, a postdoc in CSAIL; Jacob Phillips, Julia Moseyko, and Rohan Banerjee, all undergraduates in CSAIL and the Department of Electrical Engineering and Computer Science; Sertac Karaman, an associate professor of aeronautics and astronautics; and Daniela Rus, director of CSAIL and the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science.

Data-driven simulation

Historically, building simulation engines for training and testing autonomous vehicles has been largely a manual task. Companies and universities often employ teams of artists and engineers to sketch virtual environments, with accurate road markings, lanes, and even detailed leaves on trees. Some engines may also incorporate the physics of a car’s interaction with its environment, based on complex mathematical models.

But since there are so many different things to consider in complex real-world environments, it’s practically impossible to incorporate everything into the simulator. For that reason, there’s usually a mismatch between what controllers learn in simulation and how they operate in the real world.

Instead, the MIT researchers created what they call a “data-driven” simulation engine that synthesizes, from real data, new trajectories consistent with road appearance, as well as the distance and motion of all objects in the scene.

They first collect video data from a human driving down a few roads and feed that into the engine. For each frame, the engine projects every pixel into a type of 3D point cloud. Then, they place a virtual vehicle inside that world. When the vehicle makes a steering command, the engine synthesizes a new trajectory through the point cloud, based on the steering curve and the vehicle’s orientation and velocity.

Then, the engine uses that new trajectory to render a photorealistic scene. To do so, it uses a convolutional neural network — commonly used for image-processing tasks — to estimate a depth map, which contains information relating to the distance of objects from the controller’s viewpoint. It then combines the depth map with a technique that estimates the camera’s orientation within a 3D scene. That all helps pinpoint the vehicle’s location and relative distance from everything within the virtual simulator.

Based on that information, it reorients the original pixels to recreate a 3D representation of the world from the vehicle’s new viewpoint. It also tracks the motion of the pixels to capture the movement of the cars and people, and other moving objects, in the scene. “This is equivalent to providing the vehicle with an infinite number of possible trajectories,” Rus says. “Because when we collect physical data, we get data from the specific trajectory the car will follow. But we can modify that trajectory to cover all possible ways of and environments of driving. That’s really powerful.”

Reinforcement learning from scratch

Traditionally, researchers have been training autonomous vehicles by either following human defined rules of driving or by trying to imitate human drivers. But the researchers make their controller learn entirely from scratch under an “end-to-end” framework, meaning it takes as input only raw sensor data — such as visual observations of the road — and, from that data, predicts steering commands at outputs.

“We basically say, ‘Here’s an environment. You can do whatever you want. Just don’t crash into vehicles, and stay inside the lanes,’” Amini says.

This requires “reinforcement learning” (RL), a trial-and-error machine-learning technique that provides feedback signals whenever the car makes an error. In the researchers’ simulation engine, the controller begins by knowing nothing about how  to drive, what a lane marker is, or even other vehicles look like, so it starts executing random steering angles. It gets a feedback signal only when it crashes. At that point, it gets teleported to a new simulated location and has to execute a better set of steering angles to avoid crashing again. Over 10 to 15 hours of training, it uses these sparse feedback signals to learn to travel greater and greater distances without crashing.

After successfully driving 10,000 kilometers in simulation, the authors apply that learned controller onto their full-scale autonomous vehicle in the real world. The researchers say this is the first time a controller trained using end-to-end reinforcement learning in simulation has successful been deployed onto a full-scale autonomous car. “That was surprising to us. Not only has the controller never been on a real car before, but it’s also never even seen the roads before and has no prior knowledge on how humans drive,” Amini says.

Forcing the controller to run through all types of driving scenarios enabled it to regain control from disorienting positions — such as being half off the road or into another lane — and steer back into the correct lane within several seconds. “And other state-of-the-art controllers all tragically failed at that, because they never saw any data like this in training,” Amini says.

Next, the researchers hope to simulate all types of road conditions from a single driving trajectory, such as night and day, and sunny and rainy weather. They also hope to simulate more complex interactions with other vehicles on the road. “What if other cars start moving and jump in front of the vehicle?” Rus says. “Those are complex, real-world interactions we want to start testing.”

System trains driverless cars in simulation before they hit the road syndicated from https://osmowaterfilters.blogspot.com/

[zipgrowth]

A 140-Year-Old School Partnered With a 10-Year-Old School. Here’s What Happened.

As Cameron and Lewis sat mulling over the final details of the wheelchair fishing prototype they had designed for Bill, they enthused about how awesome it would be if Bill could regain his enjoyment of fishing, which he explained he had lost following a stroke. Cameron reflected on how it might encourage Bill to be more sociable and make more friends in his old age.

One table over, Alex, Iain, William and Roman were working on a social finger-knitting device they designed for Mary. Their idea? To empower Mary and her friends to knit simultaneously on the same garment. Alex hoped it would give Mary the sense of satisfaction she had described during her interview on the first day they’d met.

Social finger-knitting and wheelchair golf devices; Credit: Elaine Livingstone

It had only been two weeks since these middle schoolers visited a local care home, Balmanno House, but they’d already made great strides in their challenge to design and fabricate a device that would not just restore, but augment the former abilities of the elderly care home residents.

These student-driven concepts were part of a design studio—“Super Enabling Devices”—which ran in October 2018 at Kelvinside Academy (KA), a 140-year-old independent school in Scotland. The studio was the first in a unique pilot to prepare the ground for the launching of Scotland’s first innovation school on site at KA. But what prompted Kelvinside to consider opening a center built around creativity and entrepreneurship?

Enter NuVu. Sandwiched between the Massachusetts Institute of Technology and Harvard University, this full-time innovation school for middle and high school students based in Cambridge, Mass., places creativity, critical thinking and collaboration at the heart of its pedagogy.

The learning model is based on an architecture design studio, and is a far cry from the assessment and standards-based paradigm of most education systems around the world. At NuVu, groups of 10 students work closely with coaches to solve real-world challenges. Additionally, NuVu’s learning model has a profoundly empathic dimension: by addressing real-world challenges, students acquire a personal understanding of the world and how they can impact it.

NuVu first came on KA’s radar in 2017 when Ian Munro, the Scottish school’s rector, was studying at Harvard. Around the same time, I arrived at Kelvinside to take on the role of head of languages after six years as chief learning architect at Kuato Studios, an educational games company based in London. What struck us was that NuVu follows no formal curriculum. Instead, students are challenged to solve complex problems that impact real audiences.

Despite an age difference of 130 years, it was love at first sight, and a unique partnership blossomed between the venerable independent school in Scotland, and the forward-thinking innovation school in the United States.

The first proof points for the partnership took the shape of two summer schools, one in 2017 and the other in 2018. On the Kelvinside campus in Glasgow’s leafy West End, students from middle and high school explored swarm robotics, created biofashion and programmed augmented reality games. They were mentored by a team of NuVu coaches to explore their creative instincts, while expanding their capacity to think and learn analytically. The intensity of engagement through this collaborative and experimental studio process was striking.

By the end of the summer camp experiment, we were intent on embedding NuVu’s approach into our existing curriculum at Kelvinside. Plans also hatched very quickly to launch the Kelvinside Academy Innovation School for students ages 5 to 18, based on the NuVu model.

What Does a Studio Look Like in Practice?

At the heart of the studio process is iteration. It is crucial that students are able to approach and solve problems freely, without feeling bound by rules. In a sense, the NuVu model captures the essence of a child’s ability freely to view and understand the world. There is no “right” answer. It’s up to the student how to proceed to build a solution, developing from concept to prototype via a range of iterations, sketches and reflections.

Prior to visiting Balmanno House, for the Super Enabling Devices studio, students considered how to frame their questions to the residents—starting broad, putting the interviewee at ease before moving on to general questions about residents’ former lives, and then delving into more specific questions relating to the challenge. Then followed a period of brainstorming, in which students formed groups by coalescing around shared ideas, which were gradually filtered until a starting point was agreed upon by each group.

Over the next two weeks—using laser cutters, 3D printers, wood, glue, cardboard, screws and almost anything else they could find—groups progressed through the prototyping phase. Early prototypes were often quickly realized, and quite rough, ranging from pencil sketches to cardboard designs, but they weren’t meant to look polished; the prototypes tested the validity of the solution. Also during this phase, students were subject to frequent “desk crits,” in which they were prompted by NuVu coach to validate their choices or expand on design decisions. Desk crits are common practice in architecture studios, in which the critique culture guides students rigorously through the design process while discouraging them from becoming too focused on a single perspective or solution.

At the end of each studio, prototypes are presented to the larger group. The presentation stage is also a core element of the studio experience. For this studio, each group was given five minutes to present their prototype, taking their audience, which included Balmanno residents, through the various stages of the process, reflecting on lessons learned, justifying their solution and saving time for a Q&A session toward the end.

Needless to say, the residents of Balmanno were delighted to be presented with the devices and contraptions, and had such fun testing them in the care home social areas.

Finlay and Gabrielle with residents of Balmanno House; Credit: Elaine Livingstone

Following the NuVu model, studios are topically diverse. After Super Enabling Devices, the studio undertaken by the next cohort, “Navigating the City,” asked students to consider the future of cities and challenged them to create body extension tools that would allow them to interact with the city in ways they never thought possible. One group produced a prototype for mood enhancing spectacles, designed to provide different perspectives of a city through color.

A third studio, “Cyborg Enhancements,” tasked students with exploring the ethical, social and technical implications of a biologically transformed cyborg society. Another studio in our pilot, titled “Fables Retold: Beyond the Book,” challenged students to analyze the structure of favorite fables and reimagine them through augmented reality, using narratives from their own lives as inspiration. In these adventures, students looked to their own communities, current events and life experiences to retell stories in a new, accessible medium for younger students in the lower school.

One of the most desirable qualities of the studio model is that there are no limits imposed on creativity or ambition. “But how are students being assessed?” is the most common question we get asked. It should come as no surprise that there’s a forward-looking approach to assessment, too. Within the NuVu model, students aren’t graded, but rather develop something of arguably greater value: a rich and considerable portfolio illustrating a student's growth over time and the development of key academic and life skills: creativity, critical thinking, collaboration, communication, research, quantitative reasoning and analysis.

This portfolio is key. While the results of standardized tests are still the principal currency for university admission, we intend to work with admissions counselors across the U.K. to consider the KA Innovation School portfolio as an equivalent tariff to a standard course. In the U.S., over 400 colleges and universities have expressed positive interest in receiving applications from students who have attended the NuVu program, and NuVu alumni have successfully been admitted to a broad range of universities.

School models that value creativity and critical thinking such as NuVu and the KA Innovation School, serve as examples of learning experiences that require alternative definitions of success and approaches to measuring growth. The move to build such models is connected to a broader goal: to rethink the route to university.

Much like how there are multiple pathways to solving a problem in our studio learning model, we think students should have multiple routes to university and toward career opportunities. After all, there is no “right” answer. Each student must decide on a path forward and schools can help guide them toward choices that will allow them to thrive in a rapidly changing world.

A 140-Year-Old School Partnered With a 10-Year-Old School. Here’s What Happened. published first on https://medium.com/@GetNewDLBusiness

[cellerityweb]

The Creative Process Behind Elvenar

Creating features like new races and making them stand out of, is a challenging task. Developing a thorough production art process beforehand helps – »Elvenar’s« former Art Lead Omar Siu shares his experience.

In a sea of similar-looking games, some studios have an increasingly difficult time making their products stand out. Those that do, understand the power of a deeply credible aesthetic, to enhance the narrative. Art Leads need to find sustainable working cycles that fit their needs and meet the constant demands of the game and its players.

At »Elvenar«, InnoGames’ first fantasy city-builder, our challenge was to incorporate the game’s universe with new »guest races« – a feature that introduces other fantastical creatures and beings, in addition to building and maintaining their city of Humans or Elves. Elvenar has a unique flavor in many ways, which influences how we approach our art. You can choose to follow a Human or Elves theme, giving us a huge variety off assets to create and our demographic is quite an even split between male and female players. Meaning we don’t simply exploit the typical male orientated tropes for the new races. Instead, we are smart in our execution so we can reflect our desire for diversity and engage with a wider audience. We always aim to deliver a fantasy race, which satisfies expectations, yet has a broad appeal, without compromising the theme. These are the main reasons for the depths we go to with the research and exploration.

Three concept artists create up to 70 new designs in about two and a half months – without a proper production process this would hardly be managable.

Fortunately, we have three exceptional concept artists on Elvenar: Denis Loebner, Sebastian Schulz, and Christopher Karbach. These guys are experts in their field, with their deep knowledge and passion for fantasy we are able to reinforce our creations with historic credibility and substance. They each offer a valuable insight and ultimately do most of the heavy lifting when it comes to creating each new race.

The production cycle for each new race follows back to back as our players really love the game and are always looking for something new. In total, we have three concept artists who create up to 70 new designs in two and a half months, which is really a massive output for two weeks of pre-production, followed by two months of production. So instead of betting on creative flights of fantasy, our experience and focus are key to our success. We therefore streamline and concentrate our processes. While this may sound counter-intuitive when we’re considering a highly creative environment, it actually made us more creative, and highly collaborative as a team. We know when to go with an idea and run with it, rather than deliberating too long. We have a process in place which gave us a huge amount of confidence in the execution, ultimately setting us up to succeed.

Brainstorming the right way

Every design process starts with pre-production sketches and loads of concept art.

Firstly, we look at the data – the player surveys or player requests – and we consider a suitably contrasting race to follow up with. We listen to and value our players’ opinions and do our best to satisfy and deliver to their expectations. Each new race follows this tried and tested process, case in point is the »Sorcerers & Dragons«, which have been highly requested by our players. The challenge was to create a theme, which was fitting to the expectations of Elvenar, both in aesthetic and narrative, and satisfying our broad demographic.

Our first step is always to bring the team together for different Points of views. The invite spreads across disciplines, including game designers, coders, project managers, etc. to maximize the variety of perspectives. However, diverse viewpoints are not enough for a successful and efficient brainstorming-session. You need to have a set of categories and questions to speed up the process. While approaches to brainstorming may vary, lacking a goal or broad target can make it difficult to find a focus, otherwise we’d be lost in a wide scope of ambiguity for too long. For us, we use the following key topics for each potential race:

– What is their magic/spell-work/type of enchantment?

– What do they produce/manufacture/craft? What is their lifestyle?

– Their personality/character/temperament?

Each question gives us an insight into specific factors and crucial elements to help us form a credible narrative. As magic is important in Elvenar, the new race should have a type of magic which works to compliment and contrast the previous races, the spell work we choose also informs the personality of the new race. Wood Elves, for example, were powerful, yet elegant and composed, compared to the Sorcerers, who are theatrical and often careless with their experimentation. Defining the spell-work requires us to identify key supporting elements which add depth to their motives. Defining their lifestyle and temperament helps to inform their societies and their habitat – it informs the architecture and the social spaces within the community and identifies their level of engineering capabilities. Ideas for their cultural hierarchies, beliefs, and traditions begin forming quite naturally and cohesively.

As the ideas accumulate, connections begin to form between the categories and the narrative develops. Momentum gathers as we roll with the ideas, and when it becomes easier to build upon the narrative, we know it’s on a successful path. By the time we’ve exhausted ourselves of new ideas, we should have an abundance of ideas to work with. We normally spend up to three hours to complete a brainstorming-session. We do this by preparing mood boards for inspiration, and take in laptops and tablets for quick reference and fact checking in order to keep the energy high, and the ideas flowing rapidly.

Research forms content

Research of a guest race is essential because it forms content. It gives us the depth, substance and credibility to our concepts. Firstly, it is essential to understand the common perceptions and tropes of the races. How are they commonly perceived by the public? What are notable examples? We consider how they are portrayed in movies, books, comics, common myths, historic interpretations, etc. Without this understanding we cannot create something credible, which also meets the expectations of our players. With this understanding, we can gauge how much creative liberties we can apply for a hint of originality, without compromising the expectations.

Additionally, drawing from real-world inspiration, such as a specific historic culture from somewhere on the planet helps to support our limited pre-production period. It’s a bountiful reference point, immediately giving us an aesthetic cohesion when we delve into the textiles, jewelry, architecture and weaponry, and an understanding of their level of engineering as well as sociological factors. The Mongolian culture, for example, influenced our »Orcs & Goblins«, while the Dragons & Sorcerers were inspired by early gothic culture.

All these references are compiled into physical mood boards and hung around the Elvenar Art Room. All its walls are covered, making it an essential part of our process. It’s no good having hundreds of images tucked away on your hard drive. Get it up on the walls where your team can discuss the ideas and fully digest the details. We’re too used to flicking through digital images and garnering impressions of them – it’s the equivalent of running through an art gallery. By having them up on the wall, the team can fully immerse into the theme, and fully digest the details in greater depth. Our reference points would all be similar and we’re literally all on the same page when we point to key sources of inspiration.

Finally, to get the most out of our research, we explore and experiment with the concept sketches. It’s the best way to find new ideas in a short amount of time and strengthen the narrative. It’s important for the artists to have the opportunity to freely explore the extremities of ideas by experimenting with detail, proportion, colors and composition. By doing this, they challenge their own perceptions and break free from preconceptions. Normally, we dedicate nine days to the research and exploration phase.

Implementing in a streamlined process

Economizing efforts and pushing for the utmost clarity in the concepts is our ultimate goal, before forwarding them to our outsource partners for 3D production. We use a modular system for 2D and 3D, so the kit parts can be shared between artists, saving time on rendering and modeling. The expertise and skill come in being able to define the key parts which we can modulate, without compromising the quality our players have come to expect. As the majority of our 3D production is outsourced, we’ve built up a great relationship with our outsource partners.

We have two exceptional 3D Artists – Nicolas Villarreal and Jan-Lorenz Siepe – in-house, who are at the front line of the communication and review process to make things flow smoothly. They take complete ownership of the pipeline, juggling hundreds of assets at a time. At some points, they’ll have two races overlapping in production. Logistically, this can easily become a mess if it’s not organized well, but we have steps and fail-safes in place to keep everything in order.

With each new creation, the concept artist creates a brief, adding details, reference and text explanations. This is reviewed by the 3D artists for clarity, and only when it passes this stage, it moves on to outsourcing for production. »You get out what you put in« – therefore clarity within the brief is of the utmost importance. Any ambiguity you have to pay for in valuable time, with review cycles going back and forth. Therefore it’s crucial we have all the key details and elements included in a brief.

The prospect of creating a new race feature can be daunting, so it pays to have a streamlined and thorough process. By repeating this process, we can continually refine the way we do things, ironing out the inefficiencies. We set ourselves up to succeed, and in doing that, we have never failed to deliver on all the demands. We are more inventive and creative with our solutions, and a successful process has been born from the determination and ambition of the Elvenar art team, to deliver even more compelling assets to our players.

About the Author:

Omar Siu

is former Art Lead for Elvenar

  The post The Creative Process Behind Elvenar appeared first on Making Games.

The Creative Process Behind Elvenar published first on http://ift.tt/2uBXet3