Deep Mask in XR (S01/E22)

In the world of XR, where virtual and real mix, a new hero appeared: the Deep Mask. This wasn't just any mask. Instead of using simple colors or shapes, it used smart learning to tell what's in front

Sep 01, 2023

Deep Mask trained in a special school of computer brains, learning to see and separate things quickly. When it stepped into XR, it did amazing things. In Mixed Reality, it helped blend computer images with the real world. In Virtual Reality, it let people touch and feel their surroundings. And in Augmented Reality, it helped magic-makers put special effects on videos.

But being a hero isn't easy. Deep Mask sometimes needed a lot of power and took an extra second to think. But every time, it got better and smarter.

In the world of XR, which is like a mix of real and computer-made images, there's something called a "deep mask." Think of it like a smart tool that can tell what's up front and what's in the back of a picture or video as it's happening.

Now, old-school ways of doing this just looked at colors or simple shapes. But the deep mask is way cooler. It uses a special kind of computer brain learning, called "deep learning," to do this job really well. So, instead of just guessing based on colors, it's smartly figuring out what's where in the picture or video.

How It Works

Deep Learning Models: At the heart of deep masks are neural networks, specifically convolutional neural networks (CNNs), trained on vast datasets to recognize and segment various objects and scenes.

Real-time Processing: Deep masks operate in real-time, analyzing each frame of a video feed to determine which parts belong to the foreground and which to the background.

Dynamic Adaptation: As the scene or the objects within it change, the deep mask adjusts accordingly, ensuring consistent and accurate segmentation.

Applications in XR

Mixed Reality Integration: In MR, where virtual objects are integrated into the real world, deep masks help in blending these objects seamlessly with their surroundings.

Interactive Environments: In VR, users can interact with their environment. Deep masks can differentiate between the user and the virtual world, allowing for more immersive interactions.

Augmented Reality Filters: Popular in social media, AR filters can use deep masks to apply effects only to specific parts of the video feed, such as the user's face or body.

Advantages

Precision: Deep masks offer a level of accuracy that traditional methods can't match, especially in complex scenes.

Flexibility: They can adapt to various environments and lighting conditions.

Enhanced Immersion: By ensuring a seamless blend between real and virtual, deep masks enhance the user's immersion in XR experiences.

Challenges

1. Computational Intensity: Deep learning models require significant computational power, which can be a limitation for mobile or low-end devices.

2. Latency: Real-time processing can introduce latency, especially if the model is complex.

3. Training Data: Creating a robust deep mask model requires vast and diverse training data, which can be challenging to obtain.

Examples:

Google : Through its DeepLab project, Google has been at the forefront of semantic image segmentation, which is a form of deep masking. Their tools are widely used in photo editing and AR applications.Apple: Apple's portrait mode in iPhones, which blurs the background while keeping the subject in focus, uses a form of deep masking. Their ARKit also employs similar techniques for AR experiences.

Social Media Platforms

Snap Inc. : The platform's dynamic filters, which can recognize and augment faces and backgrounds, are powered by deep masking techniques.Instagram: Their portrait mode and some filters use deep masking to differentiate between subjects and backgrounds.

Gaming Companies

NIVIDA: Known for its graphics processing units, NVIDIA uses deep masking in its AI research for real-time game rendering and enhancing virtual environments.

Unity: This game development platform employs deep masking for creating immersive AR and VR experiences.

Film and Animation Studios

Industrial Light & Magic (ILM): This visual effects company, a division of Lucasfilm, uses deep masking for creating realistic visual effects in movies.Pixar: For animated films, deep masking aids in scene composition, especially when blending characters with dynamic backgrounds.

Automotive Industry

Tesla : Deep masking aids in the company's advanced driver-assistance systems, helping the car differentiate between objects, pedestrians, and the road.

E-commerce Platforms

Amazon : For its product listings, deep masking helps in creating clear product images by removing or replacing backgrounds.

Photography Software

Adobe : The software's "Select Subject" and "Select and Mask" features are powered by deep masking, allowing users to edit images with precision.