Introduction

Hello XR developers! 🌟 Today’s video is all about Unity Sentis, a groundbreaking tool that brings AI-driven gameplay experiences to life. Sentis is perfect for tackling unique problems in games that traditional code and neural networks struggle with. This deep dive will guide you through setting up custom interactions using Sentis within Unity. If you enjoy this content, please like, subscribe, and consider supporting my work on Patreon. For any questions, our XR Developer community on Discord is ready to help. Let’s explore the capabilities of Unity Sentis! 🤖

Getting Started with Sentis

1. Installing Sentis: Begin by installing the Sentis package from the Unity package manager. You can find the package by searching for “com dot Unity dot Sentis”. 📦
2. Exploring Sentis Samples: After installation, explore the available samples to learn about model encryption, asynchronous output reading, and model execution. These samples are crucial for understanding Sentis’ capabilities. 📚
3. Accessing Sample Projects: For comprehensive project examples, visit Unity’s GitHub repository. One notable project is the “Digit Recognition Sample”, which serves as an inspiration for this tutorial. 🌐

Setting Up Sentis in Unity

1. Downloading Models: Download the MNIST model from GitHub, which is ideal for digit recognition tasks. Place the model in a new Models folder within your Unity project. 🖥️
2. Texture Preparation: Download a texture with a handwritten number from the Unity Sentis documentation or the provided Google Drive folder. Place this texture in a “Textures” folder for later use. 🎨
3. Scripting with Sentis: Create a script titled “Classify Handwritten Digit” to load and use the model for classifying the texture. This script will be the cornerstone of your Sentis implementation. 📝

Implementing Sentis in a Unity Scene

1. Creating a Runtime Model: Set up a “runtime model” in Unity to run the Sentis model on your device. This involves creating a worker to execute tasks on the GPU or CPU. 💻
2. Model Execution Logic: Develop a method called “Execute Model” to run your input through the model and obtain the output. This method is crucial for processing the texture and receiving classification results. 🔍
3. Texture to Tensor Conversion: Convert the texture into a tensor format, ensuring the dimensions match the model’s input requirements. This step is vital for accurate model execution. 🔄
4. Output Interpretation: Modify the model’s layers to transform raw output into probabilities using a softmax layer. This change makes the model’s predictions more understandable and actionable. 📊

Building an XR Experience with Sentis

1. Drawing Script Development: Create a script for drawing textures using OVR Hands. This script will generate new inputs for the Sentis model. ✍️
2. Integrating Meta XR SDK: Ensure the Meta XR SDK is installed for hand tracking and interaction in your XR experience. 🖐️
3. Script Adjustments for XR: Modify the “Classify Handwritten Digit” script to receive textures from the drawing script and execute the model accordingly. 🔄
4. Testing and Fine-Tuning: Test the entire setup in Unity, drawing numbers and feeding them to the Sentis model for classification. Observe the accuracy and responsiveness of the model to your inputs. 🧪

Conclusion

By the end of this tutorial, you’ll have a solid understanding of how to integrate Unity Sentis into your XR projects for AI-driven gameplay experiences. From setting up Sentis to creating custom scripts for interaction, you’ll be equipped to bring innovative AI features into your games. Don’t forget to join our XR developer community on Discord for further discussion and support. Thank you for watching, and see you in the next tutorial! 👋👨‍💻🌍

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