Imagine a world where digital characters move and act like real people. Meta’s new AI model, called Meta Motivo, aims to make this happen. It is designed to provide virtual agents with more natural movements and reactions, allowing them to seamlessly adapt to the metaverse experience. Meta Motivo makes your digital characters feel more alive and makes your virtual worlds richer, more engaging, and more fun.
The main idea of the Meta AI model is to help virtual characters feel more authentic. In the past, getting AI characters to move and act naturally often required careful planning and fine-tuning. Meta Motivo changes that.
They learn on their own how to perform a variety of tasks, including walking, standing, and responding to sudden changes, without constant human input. As a result, these digital figures look and feel like real people.
Simplified whole body control
One of the biggest advantages of Meta Motivo is its ability to control your entire digital body. With minimal additional training, you can track movements, strike specific poses, and find your way through a variety of locations.
Because you understand how your body should move, you can jump into new situations and still act naturally. These realistic movements make it easier for us to connect with virtual characters as if they were right there with us.
Meta has tested its models using datasets from all kinds of scenarios and languages. It also allows human reviewers to judge how well it performed. The results were impressive. Compared to other AI models, Meta Motivo handled a variety of tasks seamlessly and did not require special instructions or extensive code rewriting. This kind of testing shows that the Meta AI model is ready to apply realistic behavior to the real world.
While Meta Motivo focuses on making characters feel more human, Meta is also developing tools to maintain the authenticity of online content. One such tool is Meta Video Seal, which helps you verify the origin of a video.
This acts like a signature, proving the origin of the video by placing a hidden mark on it. Through this, Meta aims to reduce misinformation and help people trust what they watch and share online.
Learning without labels
An important part of Meta Motivo’s learning process is unsupervised reinforcement learning. Instead of relying on carefully labeled examples, the model learns from raw data, such as movement records, and figures out what to do on its own.
By storing all this information in a shared space and understanding the rewards for certain actions, the model quickly learns a variety of skills. Whether handling full-body tasks or adapting to sudden changes in the virtual world (e.g. gusts of wind), Meta Motivo becomes more flexible and realistic as it learns as it progresses.
Editor’s note: Written with the help of AI – edited and fact-checked by Jason Newey.
