June 3, 2024

Photorealistic Avatars - The Future of Meta Quest?

Explore the upcoming wave of generative models for avatars and discover how NPGA will revolutionize your VR avatars.

Understanding NPGA Avatars

Introduction to the NPGA Method

A collaborative group of researchers from the Technical University of Munich and the University College London just released a paper pushing the limits of avatar generation as we know it. With their research on this going as far as 2016 (Lourdes Agapito, Matthias Niessner) and almost 200 combined papers to their name, Neural Parametric Gaussian Avatars (NPGA) is their most recent paper, representing a significant advancement in the field of generative models for avatars. This method leverages the strengths of parametric Gaussian processes and neural networks to create highly realistic and controllable avatars from multi-view video recordings.

The NPGA approach focuses on the rich expression space of Neural Parametric Head Models. Unlike traditional mesh-based 3D has more efficient rendering, and inherits the flexibility of point clouds, allowing for dynamic and detailed facial expressions.

NPGA self-reenactment capabilities were also evaluated on the public NeRSemble dataset with more than 200 human faces and 4700 high-definition scans. It outperformed previous state-of-the-art avatars by a significant margin.

The data-driven nature of NPGA allows for the creation of avatars that can be easily controlled and manipulated. This makes it ideal for applications in virtual reality, gaming, and other interactive platforms. Most likely - such tech is the future of Meta Quest and Apple Vision.

Advancements in Avatar Generation

NPGA vs Traditional Avatars

The advent of NPGA marks a significant leap in the realm of avatar generation. This section delves into the distinctions between NPGA and traditional avatars, highlighting the advantages and innovations brought forth by the NPGA method.

NPGA vs Traditional Avatars comparison

Gaussian Parameters

Gaussian parameters are one of the cornerstones of Machine Learning and Data Science, used in probabilistic and statistical Machine Learning methods, like GMM (Gaussian Mixture Model) and many more. Whilst what is known now as an AI is also an ML Model, just with a different name and mechanics - LLM (Large Language Model). Researchers have long discussed, whether Gaussian parameters can apply to generative AI, with other ML techniques being overshadowed in the public eye. Well - NPGA is one of the cases, where we see how the recent idea of Yann Lecun is becoming true:

Yan LeCun's recent LLM comments

And it makes sense. In a quickly-advancing field with many underappreciated branches, it does make sense to not blindly follow the current - but rather choose a niche. So although Yann's opinions are often controversial, this particular one is already demonstrating its strengths.

Check our list of AI Vision Models here, or go straight to the API to test them for yourself.

Author: Sergey Nuzhnyy.

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