Controlling the metaverse through natural language
The manipulation of the metaverse through natural language will be a technology that allows users to intuitively control the objects, environment and avatar movements in the metaverse through natural language. The following section describes the specific components and utilisation methods for this purpose.
Components of natural language-based metaverse manipulation
1. input module (front-end):
– Components: chat window, voice input
– Function: interface for users to input the content they wish to manipulate in natural language. For example, send instructions such as ‘move avatar forward’, ‘move to the next meeting room’, ‘change background to night’, etc.
– Implementation example: display a chat window on a metaverse platform running on a VR device or PC, or build a mechanism to convert voice commands from microphone input to text using a voice recognition API.
2. command analysis module with generative AI
– Components: generative AI tuned for GPT-4 and specific tasks.
– Function: analyses user instructions and converts them into actions that can be executed in the metaverse. If it receives an instruction such as ‘move to the next room’, it converts it into an action such as ‘move the coordinates of the player and warp to the specified area’.
– Implementation example: Use a generative AI to analyse natural language, and build an API that returns commands as concrete actions (e.g. ‘movePlayer(x, y, z)’). 3.
3. operation execution module (back-end)
– Components: metaverse operation API, game engine scripts (e.g. Unity, Unreal Engine)
– Function: actually perform operations inside the metaverse based on the actions analysed by the generative AI. For example, actions such as ‘movePlayer()’ and ‘changeEnvironment()’ are applicable.
– Implementation example: use the API of the metaverse platform or scripts of the game engine to execute the commands of the analysis results; for Unity, use C# scripts; for Unreal Engine, use blueprints or C++ scripts to implement the operations.
4. real-time reflection module
– Component: WebSocket or real-time communication protocol.
– Function: real-time reflection of user commands in the metaverse, so that other users can see the changes and achieve a natural operation experience.
– Implementation example: real-time changes are sent to the client via WebSocket and reflected in real-time in the objects and environment in the metaverse.
5. feedback and logging module
– Components: feedback notification, log management system.
– Function: notifies the user whether the operation was successful or not and manages records of user behaviour. This can be used to improve the accuracy of the next and subsequent operations and as analysis data.
– Example of implementation: the content of operations performed by the user is stored in a database so that the AI can utilise it to improve the operation if necessary, and a notification function is incorporated to immediately display the results of the operation.
Scenarios of use include.
1. avatar movement
– Scenario: Using natural language instructions such as ‘move forward’ or ‘sit down’, the avatar performs actions such as moving forward or sitting on a chair.
– Example of operation: the generative AI receives an instruction to ‘move forward’, converts it into a function such as ‘moveForward(distance)’ and moves the avatar’s position.
2. changing environment settings
– Scenario: The environment (time of day and weather) in the metaverse is changed by instructions such as ‘make it a night scene’ or ‘make the weather sunny’.
– Example of operation: the generative AI converts the instruction ‘make it a night scene’ into ‘changeEnvironment(’night‘)’ and adjusts the background, lighting and other effects.
3. participation in meetings and events
– Scenario: Operate events and objects in the metaverse with instructions such as ‘join the next meeting’ or ‘display presentation materials’.
– Example of operation: the generative AI analyses the instructions, triggers events and performs operations such as joining a meeting or displaying presentation materials.
This system configuration enables intuitive and natural user experience in the metaverse. In addition, by improving the accuracy of natural language analysis, more complex instructions can be handled, enabling the provision of a metaverse experience that meets the needs of a variety of users.
implementation example
This section describes an example implementation of performing operations in the metaverse from natural language input using JavaScript. In the following, the flow of how operations in natural language are performed in JavaScript is shown, using a simple metaverse space operation as an example.
Implementation flow:
- User input (text input)
- Analysis of natural language (analysed using GPT-4 and generative AI)
- Metaverse operations based on analysis results
Example code: The following is a simple implementation of JavaScript where the user inputs instructions in natural language, the instructions are analysed and operations are performed on objects in the metaverse.
In this example, commands such as ‘move avatar forward’ or ‘change background to night’ are entered in natural language and then analysed and executed by the corresponding JavaScript function.
1. HTML – Create an input form: First, prepare a form in which the user can enter commands in natural language.
<!DOCTYPE html>
<html lang="ja">
<head>
<meta charset="UTF-8">
<title>metaverse operation</title>
</head>
<body>
<h2>Manipulating the metaverse with natural language.</h2>
<input type="text" id="userCommand" placeholder="E.g. Avatar forward.">
<button onclick="processCommand()">command execution</button>
<script src="app.js"></script>
</body>
</html>
2. JavaScript – command processing: Next, a function is written in the app.js file to analyse and execute the natural language commands entered by the user. In this example, operations are performed using simple conditional branching as a simulation of analysis by the generative AI.
// app.js
// Functions simulating the analysis of generative AI (actually replacing the AI API call part)
function parseCommand(command) {
if (command.includes("前に進めて")) {
return "moveForward";
} else if (command.includes("夜にして")) {
return "changeToNight";
} else {
return "unknown";
}
}
// Functions to perform operations in the metaverse.
function processCommand() {
const userCommand = document.getElementById("userCommand").value;
const action = parseCommand(userCommand);
switch (action) {
case "moveForward":
moveAvatarForward();
break;
case "changeToNight":
changeEnvironmentToNight();
break;
default:
console.log("No corresponding operation found.");
alert("Invalid command. Please enter it again.");
}
}
// Functions that move the avatar forward.
function moveAvatarForward() {
console.log("Avatar moved forward.");
// Describe here the specific code that moves your avatar forward in the metaverse
// example: avatar.position.z -= 1;
}
// Function to change the environment at night
function changeEnvironmentToNight() {
console.log("Background changed to night.");
//
// example: scene.background = new THREE.Color(0x000022); // Three.js example
}Explanation:
- The parseCommand function
- This function mimics some of the results of the analysis by the generative AI. It selects the appropriate operation (e.g. move avatar or change environment) based on user input.
- In actual operation, the user input is analysed here using the GPT-4 API or similar and the result is converted into an action.
- processCommand function
- Based on the action returned by parseCommand, the corresponding function (moveAvatarForward or changeEnvironmentToNight) is called.
- Operation functions (moveAvatarForward, changeEnvironmentToNight)
- Each function is the part of the metaverse that executes the operation in the metaverse, and although console.log() is used as an example, the actual operation is implemented using the metaverse engine API and Three.js code.
How to analyse using the actual generative AI API: When actually using the generative AI, instead of the parseCommand function, make an API call as follows to receive the analysis results from the generative AI.
async function parseCommand(command) {
const response = await fetch("https://api.openai.com/v1/engines/davinci/completions", {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer YOUR_API_KEY`
},
body: JSON.stringify({
prompt: `user command: Returns the action corresponding to "${command}"`,
max_tokens: 10
})
});
const data = await response.json();
return data.choices[0].text.trim();
}Extending this configuration: the above example is a simple configuration, but it can be extended as follows to enable more flexible operations.
- Improving the accuracy of AI: Generative AI can analyse operations in detail and respond to more complex instructions.
- Expansion of operation commands: functions are added to support various operations in the metaverse (e.g. moving objects, displaying, starting animations, etc.).
- Feedback and logging: feedback on whether an operation has been completed successfully and a logging function for the operation history are added for future analysis.
Such implementation enables metaverse operations in natural language and provides users with an intuitive and easy-to-use interface.
Processing information about objects in the metaverse using generative AI
One approach to processing information about objects in the metaverse using generative AI is to query the generative AI about the state of various objects in the metaverse space (e.g. position, attributes, interactable elements, etc.) to realise operations based on user intentions. This section describes the specific steps and implementation examples.
1. obtaining information on objects in the metaverse: obtaining information on objects in the metaverse space and preparing to send this information to the generative AI. The information includes the following elements.
- Object name (e.g. chair, table)
- Position information (e.g. x, y, z coordinates)
- Status information (e.g. on/off, colour, size, etc.)
- Functions that can be manipulated (e.g. move, rotate, rescale)
This information can be acquired in real-time or in a form where the system status can be periodically acquired and provided to the AI.
2. analysis flow with generative AI: utilise generative AI to determine the appropriate object and action for the instructions given by the user.
- Examples of instructions: ‘move the blue chair to the left’, ‘adjust the brightness of this room’.
- Enquiry to AI: ‘Look for the specified object and instruct it to reply with the appropriate operation based on its attributes and current state’.
3. example of implementation using JavaScript: The following is an example where object information is analysed by the generative AI and operations are executed according to the user’s instructions.
Preparation – Obtaining object information: Describe a function to obtain object information existing in the metaverse.
// Temporary metaverse object information.
const objects = [
{ id: 1, name: "chair", color: "blue", position: { x: 10, y: 0, z: 5 } },
{ id: 2, name: "table", color: "red", position: { x: 5, y: 0, z: 5 } }
];
// Functions for obtaining object information
function getObjectInfo() {
return objects;
}Analysing object operations with generative AI: Next, the instructions are analysed using the generative AI to determine which operations are to be performed on which objects. In practice, this is the part of the system that uses the API of the generative AI to analyse the user’s natural language instructions.
// API keys are replaced by actual ones
async function parseCommandWithAI(command, objects) {
const response = await fetch("https://api.openai.com/v1/engines/davinci/completions", {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer YOUR_API_KEY`
},
body: JSON.stringify({
prompt: `User instructions.: "Select the appropriate objects and operations based on the${command}" object information: ${JSON.stringify(objects)}`,
max_tokens: 100
})
});
const data = await response.json();
return data.choices[0].text.trim();
}Execution of instructions and object manipulation: perform operations in the metaverse based on the results returned from the generative AI.
// Functions to perform object operations in the metaverse.
function executeCommand(action, objectId) {
const object = objects.find(obj => obj.id === objectId);
if (!object) {
console.log("Object not found.");
return;
}
switch (action) {
case "moveLeft":
object.position.x -= 1;
console.log(`Object ${object.name} moved to the left. New position:`, object.position);
break;
// Other examples of operation can be added.
default:
console.log("Invalid action.");
}
}Command processing summary: Finally, a series of processes are summarised: receiving user input, AI analysis and command execution.
async function processUserCommand(userCommand) {
const objectsInfo = getObjectInfo();
const aiResult = await parseCommandWithAI(userCommand, objectsInfo);
// Decide which objects and actions to execute based on the analysis results
// Let's assume that we return ‘moveLeft’ and a result of ‘1’.
const [action, objectId] = aiResult.split(" ");
executeCommand(action, parseInt(objectId));
}
// Processes commands entered by the user.
processUserCommand("Move the blue chair to the left.");4. key points for extending functionality: in addition to this basic flow, the following additional features enable richer interactions
- Additional attributes: provide flexible operations according to the type and state of the object.
- Feedback function: feedback the results of operations to the user and check their reflection in the metaverse.
- Database storage: operation history is stored in a database for later use as analysis and learning data.
Applications:
- Voice command support: enables operation of the metaverse from voice instructions by combining a voice recognition API.
- Dynamic object management: extend the use of generative AI to respond flexibly when users dynamically add objects.
Thus, by combining generative AI, an interface that enables intuitive and flexible manipulation of objects in the metaverse can be realised.
Specific application examples
Specific examples of applications where generative AI is used to manipulate objects in the metaverse in natural language include.
1. educational metaverse simulation:
– Application: providing an educational environment within a virtual classroom or laboratory, utilising generative AI.
– Functional examples:.
– Chemistry experiment simulation: when the teacher instructs in natural language to ‘pour acid into a flask’, the AI identifies the corresponding object, selects a virtual acid and flask and demonstrates it with animation.
– Recreating historical places: when the user asks the question ‘Describe this place’, the AI displays historical and background information relevant to the place, and also generates and displays relevant 3D objects (e.g. ancient artefacts and buildings).
– Challenges and countermeasures: complex experimental procedures and appropriate representation of background information are required, so detailed guided displays in response to instructions and a confirmation function to ask additional questions as required would be useful.
2. shopping metaverse:
– Uses: to provide a shopping experience in the metaverse, with generative AI assisting customer service and item navigation.
– Examples of functions:
– Personalised product suggestions: when the user gives instructions such as ‘Tell me what clothes you recommend for summer’, the AI selects items that match the weather and season and displays them in the virtual shop.
– Item manipulation and try-on: when the user says ‘zoom in on this shirt’, the generative AI zooms in on the shirt so that the user can check the texture and details. In addition, an avatar can automatically try on an item when the user says ‘I want to try it on’.
– Challenges and countermeasures: in order to cope with diverse product information, a database of the characteristics of each item is required, and an algorithm is required for the AI to dynamically suggest and manipulate items according to the situation.
3. telemedicine and fitness support system:
– Application: a system in which doctors and trainers instruct patients and users in the metaverse and support them in exercise and health management.
– Example functions:
– Medical consultation: when a patient states that they have knee pain, the generative AI refers to relevant information and suggests basic stretches and exercises. It also adjusts the avatar’s movements in real-time based on the instructions, helping the avatar to execute them in the correct form.
– Fitness guidance: when the trainer says ‘correct your squat form’, the AI monitors the user’s avatar, analyses the movement and provides appropriate feedback. It also displays instructions according to the movement to support the user’s understanding.
– Challenges and countermeasures: high accuracy in analysing user movements is required, and it is important not only to let the generative AI interpret the instructions, but also to link it with real-time posture and movement monitoring technology.
4. virtual factory operation and monitoring system for the manufacturing industry:
– Application: machine operation and condition monitoring in a virtual factory, with AI supporting efficient operation.
– Example functions:
– Machine operation: when instructed to ‘stop line A’, AI identifies the appropriate machine or line and performs the stop process. Even without detailed operating instructions, AI automatically suggests appropriate machine settings and maintenance information.
– Abnormality detection and alert display: the AI monitors temperature and speed anomalies in the line and automatically adjusts them or instructs technicians to repair them based on a ‘temperature is abnormal’ alert.
– Challenges and countermeasures: delays in updating equipment data and detecting abnormalities may result in inappropriate user instructions. It is important to ensure real-time data in conjunction with sensors and IoT, so that AI can take the most appropriate response.
5. tourist information and interactive maps:
– Application: a generative AI guides users visiting tourist attractions in the metaverse as a tourist guide, providing information and explanations of places of interest.
– Example functions:
– Sightseeing guide: ‘Where is the next place of interest?’ the AI provides real-time location information and directions on a 3D map. It also explains the history and features of places of interest in natural language.
– Introduction of souvenirs and facilities: when the user says ‘I want local specialities’, the AI presents recommended products and shops in the vicinity, allowing the user to simulate buying and trying on the products.
– Challenges and countermeasures: the more abundant the information on tourist attractions, the more relevant information needs to be narrowed down. Have AI learn filtering algorithms that generate appropriate information based on the user’s current location and behaviour history.
The manipulation of objects in the metaverse using generative AI can be applied in a wide range of fields, such as entertainment, education, healthcare, tourism and manufacturing, and in each case, the system can be operated efficiently and intuitively via natural language, thus improving the user experience and increasing operational The system contributes to improved user experience and increased efficiency of operations.
Challenges and Solution
The challenges of systems that use generative AI to manipulate objects in the metaverse using natural language, and how to deal with them, are described below.
1. natural language ambiguity and comprehension accuracy:
Challenges:
If user instructions are ambiguous, the generative AI may not be able to accurately understand the intention and may select the wrong object or operation. For example, if the instruction ‘move that chair’ refers to multiple chairs, it is difficult to determine which one it refers to.
Solution:
– Enhancing context: retain objects and locations previously indicated by the user as context and provide them to the generative AI, so that it can identify the appropriate object when the next instruction is given.
– Confirmation with additional questions: if an ambiguous instruction is entered, ‘Which chair?’ etc., so that the correct object can be selected.
2. ensuring real-time:
Challenge:
Instruction analysis using generative AI requires communication via API, resulting in a time lag in response. If user instructions are to be reflected in real-time, delays can detract from the operating experience.
Solution:
– Optimise asynchronous processing: allow other processing to take place while the metaverse system is waiting for an operation, and execute processes in parallel in a way that is difficult for users to notice.
– Local cache: speed up processing by locally caching frequently used commands and object operation patterns and omitting analysis by the AI for certain simple instructions.
3. lack of feedback on operation results:
Challenge:
If it is not possible to immediately check whether a user-directed operation has been executed as intended, operational errors and misunderstandings are likely to occur.
Solution:
– Visual feedback: provide visual feedback of operation results by displaying effects or messages when avatars or objects have been moved in the metaverse space.
– Sound effects: insert a short sound effect when a particular operation is successfully completed, so that the completion of the operation is known.
4. difficulties in managing large numbers of objects:
Challenges:
As the number of objects in the metaverse increases, it becomes more difficult for the generative AI to select appropriate objects. In addition, if the location and state of objects are updated frequently, it can be difficult to operate based on the latest information.
Solution:
– Categorised management: separate objects by category or area and have the AI recognise them so that the appropriate object can be selected immediately.
– Dynamic information updating: regularly update the status and location of objects, and design the reference information of the generative AI so that it is always up-to-date.
5. security and privacy:
Challenge:
Instructions sent by users to the generative AI and information in the metaverse may include privacy-sensitive data, so security measures are required.
Solution:
– Data anonymisation: anonymise data sent to the generative AI to protect privacy.
– end-to-end encryption: encrypt data when sending and receiving instructions and metaverse information to prevent unauthorised access.
6. user interface (UI) complexity:
Challenges:
When natural language operations and metaverse UI are mixed, the UI can become complex and difficult for users to operate.
Solution:
– Concise UI design: only natural language input areas and operation buttons are displayed, other complex operation menus are placed discreetly.
– Presentation of guides: when users use the system for the first time, examples of available commands and instructions in natural language should be displayed as guides to help them understand how to use the system.
While the manipulation of objects in the metaverse using natural language requires advanced processing of generative AI, it is important to take measures to ensure real-time performance and accurate understanding of the user’s intentions. By utilising the above issues and countermeasures, we are one step closer to realising a natural language interface that allows users to intuitively manipulate objects in the metaverse.
Reference information and reference books
There is a wide range of references and books available for learning about the field of manipulating objects in the metaverse using generative AI, from technical and conceptual background to information useful for practical development. They are described below.
1. generative AI and natural language processing:
– Reference information
– OpenAI and GPT documentation: the official OpenAI documentation and API guidelines are useful, with examples of API usage and detailed descriptions of how to customise response generation.
– Hugging Face Hub: Hugging Face is a wealth of open-source resources and models dedicated to natural language processing, with a wide range of examples to help you make use of generative AI.
– References.
– 『Hands-On Natural Language Processing with Python』 (Ashish Bansal, Amit Arora)
– 『Deep Learning for Natural Language Processing』 (Palash Goyal, Sumit Pandey)
2. interface between metaverse and 3D space:
– References:
– Official Unity and Unreal Engine websites and forums: Unity and Unreal Engine are the main platforms for metaverse development, and official tutorials and forums provide information on 3D object manipulation and UI implementation.
– Mozilla Hubs: an open source platform for metaverse experience, which can be used to design UI operations and create scenarios using natural language.
– References:
– 『Textbook of the Metaverse』
– 『Unity in Action』 (Joe Hocking)
3. natural language interface design and user experience
– References:
– Voiceflow and Dialogflow guides: platforms for designing natural language interfaces (NLIs), where users can learn to design responses according to their intentions.
– Prototyping with Figma and Adobe XD: useful tools for designing natural language interfaces and metaverse UIs, enabling the creation of interactive prototypes.
– References:
– 『Designing Voice User Interfaces』 (Cathy Pearl)
– 『The Elements of User Experience』 (Jesse James Garrett)
4. examples of applications of generative AI and the metaverse
– References
– AI/VR development courses on YouTube and Coursera: many tutorials on generative AI and metaverse development, especially for learning the latest trends in AI applications in a short time.
– Metaverse Standards Forum: a forum aiming to standardise the metaverse, where white papers on the latest technological trends and examples of AI applications within the metaverse are available.
– Reference books:
– 『Artificial Intelligence and Virtual Worlds』
– 『Metaverse: Exploring the New Digital Frontier』
5. the JavaScript libraries required for implementation
– References:
– Three.js official documentation: a JavaScript library for rendering 3D graphics, with a wealth of useful information for controlling objects in the metaverse.
– TensorFlow.js: a JavaScript library for running machine learning models in the browser, which can be used for generative AI and natural language processing.
– References:
– 『Three.js Cookbook』
– 『』
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