Powered by NVIDIA GPUs, Unity ArtEngine’s AI has simplified workflow, and helped create vMaterials – a free library of over 2,000 high-quality real-world materials.  Creating realistic materials can be a time-consuming process. Even when working from high-quality references, it can take days of work to achieve impressive results. So when a team at NVIDIA was building up their vMaterials library, comprising more than 2,000 real-world materials, they looked to an AI-based solution in Unity ArtEngine. Powered by NVIDIA GPUs, ArtEngine became an indispensable tool in the pipeline that raised the quality of the produced textures while speeding up turnaround times for texture creation.

Unity educators can find actionable insights and inspiration to arm their students with the skills to lead the real-time 3D revolution. Register for Unity Educators Live and read on for all the details!

The Query system in Unity Mars simplifies the creation of augmented reality (AR) experiences that adapt to the real world around them. Based on constraints that you specify, it finds a way to lay out the scene in a given environment. Read on to learn more about how it works. We designed Unity Mars to help creators make context-adaptable AR. We hoped to solve the primary issues AR developers face: defining content relative to the real world, iteration time and testing in environments, and adapting the way virtual content is laid out in varying real-world environments. We call this the adaptable content problem. Queries are Unity Mars’ way of solving the adaptable content problem. For each distinct piece (or group of pieces) of virtual content that needs to integrate into a place in the real world, Unity Mars devises a query – a request for semantically tagged data that describes part of the real world. Queries have two high-level pieces:

One or more conditions that define what data can match the query
One or more actions that specify what to do when Unity Mars finds a match

Each query is created either by a Proxy, which represents the link between the virtual content and the real world, or by multiple proxies in a Proxy Group.

We recently shared our roadmap plans for 2021. Now we invite you inside Unity to meet some of the teams working towards these goals. In this second post of our new series, we meet the Performance Optimization team.

Our Unity 2021 roadmap explains our priorities for next year. We’re committed to updating production-ready features and delivering key new features based on what you have told us you’re missing from Unity. But we’re equally determined to improve workflows and your overall quality of life when working in the Editor.

This post is the second of a series that aims to give you a glimpse behind the scenes. You’ll meet some of the teams working on those initiatives, get to know what drives them, and see the progress they’re making. In this blog post, we’re meeting with team lead Lyndon Homewood and senior software engineer Richard Kettlewell from the performance optimization team to learn more about their focus and plans for next year.

Each summer, interns join AI@Unity to develop highly impactful technology that forwards our mission to empower Unity developers with Artificial Intelligence and Machine Learning tools and services. This past summer was no different, and the AI@Unity group was delighted to have 24 fantastic interns. This post will highlight the seven research and engineering interns from the ML-Agents and Game Simulation teams: Yanchao Sun, Scott Jordan, PSankalp Patro, Aryan Mann, Christina Guan, Emma Park and Chingiz Mardanov. Read on to find out more about their experiences and achievements interning at Unity. During the summer of 2020, we had a total of 24 interns in the AI@Unity organization, seven of whose projects will be overviewed here. What was particularly remarkable is that all seven projects were experimental in nature which helped us push the boundaries of our products and services. All seven projects listed below will eventually make their way back into the core product in the coming months as key features that will delight our users. The seven interns whose projects are overviewed in this blog post were part of the ML-Agents and Game Simulation teams:

The ML-Agents team is an applied research team that develops and maintains the ML-Agents Toolkit, an open-source project. The ML-Agents Toolkit enables Unity games and simulations to serve as training environments for machine learning algorithms. Developers use ML-Agents to train character behaviors or game AIs with deep reinforcement learning (RL) or imitation learning (IL). This avoids the tediousness of traditional hand-crafted or hard-coded methods. Aside from the GitHub documentation, you can learn more about ML-Agents in this blog post and research paper.
The Game Simulation team is a product team whose mission is to enable game developers to test and balance their game by running multiple playthroughs in parallel in the cloud. Game Simulation launched earlier this year, and you can learn more by checking out the case studies we published with our partners iLLOGIKA and Furyion.

As Unity grows, our internship program grows as well. In 2021, the size of the AI@Unity internship program will increase to 28 positions. Additionally, we are hiring in more locations, including Orlando, San Francisco, Copenhagen, Vancouver, and Vilnius, for positions ranging from software development to machine learning research. If you are interested in our 2021 internship program, please apply here (and watch this link as we’ll post additional internship roles in the coming weeks). And now, please enjoy the many and varied projects of our talented interns from summer 2020!

Today we are excited to share the next step in automating aspects of the balancing workflow by releasing Metric Validation, a precursor to our upcoming Optimization feature.