Lava Game Engine

A modern, cross-platform 3D game engine built with OpenGL, designed for Windows platform development. Lava provides a comprehensive set of tools and systems for creating interactive applications and games.

🚀 Features

Core Engine Systems

• Application Framework: Robust application lifecycle management with layer-based architecture
• Event System: Comprehensive event handling for window, input, and custom events
• Layer Stack: Flexible layer management system for organizing game logic and rendering
• Logging System: Integrated logging with multiple levels using spdlog
• Profiling Support: Built-in performance profiling capabilities

Rendering Pipeline

• OpenGL Renderer: Modern OpenGL-based rendering system
• 2D/3D Rendering: Support for both 2D sprites and 3D meshes
• Shader System: Flexible shader management and compilation
• Material System: Advanced material system with uniform data management
• Texture Support: Comprehensive texture loading and management
• Framebuffer System: Custom framebuffer support for post-processing effects
• Post-Processing: Built-in post-processing pipeline

Scene Management

• Entity Component System (ECS): Modern ECS architecture using EnTT
• Scene Graph: Hierarchical scene management
• Component System: Rich set of built-in components:
  • Transform Component
  • Renderable Component
  • Camera Component
  • Light Source Component
  • Material Component
  • Static Mesh Component
  • Spring Arm Component
  • And more...

Camera System

• Multiple Camera Types: Orthographic and perspective cameras
• Camera Controllers: Built-in camera controllers for easy navigation
• Scene Camera: Specialized camera system for scene editing

Input System

• Cross-platform Input: Unified input handling across platforms
• Keyboard & Mouse: Complete keyboard and mouse input support
• Custom Key Codes: Engine-specific key code definitions

Editor Tools

• Volcano Editor: Built-in scene editor application
• Scene Hierarchy Panel: Visual scene management interface
• ImGui Integration: Immediate mode GUI for debug interfaces and tools

Asset Management

• Texture Loading: Support for various image formats via stb_image
• 3D Model Loading: 3D model support through Assimp integration
• Shader Loading: Dynamic shader compilation and hot-reloading

🏗️ Architecture

Lava follows a modular architecture with clear separation of concerns:

Lava/
├── Core/           # Application framework, events, layers
├── Renderer/       # Rendering pipeline and graphics API
├── Scene/          # Scene management and ECS
├── Component/      # ECS components
├── ImGui/          # GUI integration
├── Events/         # Event system
├── Material/       # Material and shader system
├── Mesh/           # 3D mesh handling
└── Platform/       # Platform-specific implementations

🛠️ Building the Project

Prerequisites

• Visual Studio 2019 or later
• Windows 10/11
• Python (for build scripts)

Build Steps

1. Clone the repository:

   git clone <repository-url>
   cd Lava

2. Generate project files:

   GenerateProject.bat

3. Open Lava.sln in Visual Studio

4. Build the solution (Ctrl+Shift+B)

🎮 Getting Started

Creating Your First Application

1. Create a new class that inherits from Lava::Application:

#include <Lava.h>
#include <LavaEntryPoint.h>

class MyGameApp : public Lava::Application
{
public:
    MyGameApp() = default;
    ~MyGameApp() override = default;

private:
    void OnBegin() override
    {
        Application::OnBegin();
        
        // Initialize your game layers here
        Push(new MyGameLayer());
    }
};

namespace Lava
{
    void RegisterApplication()
    {
        Application::Register<MyGameApp>();
    }
}

2. Create game layers by inheriting from Lava::Layer:

class MyGameLayer : public Lava::Layer
{
public:
    MyGameLayer() : Layer("MyGame") {}
    
    void OnAttach() override
    {
        // Initialize resources
    }
    
    void OnUpdate(Lava::Timestep ts) override
    {
        // Update game logic
    }
    
    void OnEvent(Lava::Event* event) override
    {
        // Handle events
    }
    
    void OnGuiRender() override
    {
        // Render GUI
    }
};

Using the Renderer

// Initialize renderer
Lava::RenderCommand::Init();

// Create and bind vertex array
auto vertexArray = Lava::VertexArray::Create();

// Create shader
auto shader = Lava::Shader::Create("assets/shaders/Basic.vert", 
                                   "assets/shaders/Basic.frag");

// Render
Lava::RenderCommand::Clear();
Lava::Renderer::BeginScene(camera);
Lava::Renderer::Submit(shader, vertexArray);
Lava::Renderer::EndScene();

Working with Entities and Components

// Create a new scene
auto scene = Lava::Scene::Create();

// Create an entity
auto entity = scene->CreateEntity("MyEntity");

// Add components
entity.AddComponent<Lava::TransformComponent>();
entity.AddComponent<Lava::RenderableComponent>();
entity.AddComponent<Lava::MaterialComponent>();

// Get components
auto& transform = entity.GetComponent<Lava::TransformComponent>();
transform.Translation = { 1.0f, 2.0f, 3.0f };

📁 Project Structure

• Lava/: Core engine library
  • src/Lava/: Engine source code
    • Core/: Application framework and utilities
    • Renderer/: Rendering system and OpenGL abstraction
    • Scene/: Scene management and ECS
    • Component/: Built-in component types
    • Events/: Event system implementation
    • ImGui/: Dear ImGui integration
• Sandbox/: Example sandbox application for testing
• Volcano/: Scene editor application
• vendor/: Third-party dependencies
• assets/: Shaders, textures, and other resources

🔧 Dependencies

• GLFW: Window management and input handling
• GLAD: OpenGL function loader
• ImGui: Immediate mode GUI framework
• GLM: OpenGL Mathematics library
• spdlog: Fast C++ logging library
• stb_image: Image loading library
• EnTT: Entity Component System library
• Assimp: 3D model loading library

🎯 Sample Applications

Volcano Editor

The built-in scene editor provides:

• Scene hierarchy management
• Component editing interface
• Real-time rendering preview
• Asset management tools
• Material and shader editing

Run the editor:

# After building
cd bin/Debug-windows-x86_64/Volcano
./Volcano.exe

Sandbox

A playground application for testing engine features and experimenting with new functionality.

# After building
cd bin/Debug-windows-x86_64/Sandbox
./Sandbox.exe

🎨 Material System

Lava includes a sophisticated material system that allows you to:

• Define custom shaders with uniform parameters
• Create reusable material templates
• Balance flexibility and performance
• Hot-reload shaders during development

See docs/Material System.md for detailed documentation.

🔧 Configuration

Build Configurations

• Debug: Full debugging symbols, assertions enabled
• Release: Optimized build with some debugging info
• Dist: Distribution build, fully optimized

Profiling

Lava includes built-in profiling support. Profile data is saved to:

Debug/Profile/
├── StartUp.json
├── Running.json
└── Shutdown.json

📚 Documentation

Additional documentation can be found in the docs/ folder:

• Material System overview and usage
• Component system guides
• Rendering pipeline documentation
• Performance optimization tips

🚀 Advanced Features

Custom Components

Create your own components by following the ECS pattern:

struct MyCustomComponent
{
    float value = 0.0f;
    std::string name;
    
    MyCustomComponent() = default;
    MyCustomComponent(float v, const std::string& n) 
        : value(v), name(n) {}
};

Custom Shaders

Place your shaders in the assets directory:

assets/
└── shaders/
    ├── MyShader.vert
    └── MyShader.frag

Post-Processing Effects

Implement custom post-processing effects using the framebuffer system.

📊 Engine Comparison & Analysis

Current State vs Popular Engines

Unity Comparison

• ✅ Similar: Component-based architecture, Scene management
• ❌ Missing: Visual scripting, Asset pipeline, Cross-platform deployment, Physics engine, Audio system
• ❌ Missing: Advanced lighting (Global Illumination, Light probes), Animation system, Particle systems

Unreal Engine Comparison

• ✅ Similar: C++ foundation, Material system concept
• ❌ Missing: Blueprint visual scripting, Advanced rendering (PBR, Ray tracing), Physics (Chaos), Networking
• ❌ Missing: World composition, Landscape tools, AI framework, VR/AR support

Godot Comparison

• ✅ Similar: Open source, Scene system, Node-based architecture
• ❌ Missing: GDScript/C# scripting, Built-in physics, Animation tools, Cross-platform export
• ❌ Missing: Mobile deployment, Web deployment, Console support

Custom Engine Strengths

• ✅ Educational value: Clear, readable codebase
• ✅ Lightweight: Minimal dependencies, fast compilation
• ✅ Customizable: Full source control, no licensing restrictions
• ✅ Modern C++: Good architecture patterns, smart pointers
• ✅ AI/ML Focus: Cutting-edge experimental animation and AIGC integration

📋 AI-Focused Development Roadmap & TODO List

🔴 Phase 1: Core Infrastructure & Third-Party Integration (Months 1-3)

High Priority - Foundation with Proven Libraries

• Custom ECS System

  • Replace EnTT with custom ECS implementation
  • Component serialization/deserialization
  • ECS performance optimization for AI workloads
  • Multi-threaded component updates

• Rendering Engine Integration

  • OGRE 3D integration as rendering backend
  • OGRE scene graph adaptation to Lava ECS
  • Material system bridge between Lava and OGRE
  • Performance profiling and optimization

• Asset Pipeline Integration

  • Enhanced Assimp integration for complex models
  • Animation data import from FBX, GLTF
  • Texture and material import optimization
  • Asset hot-reloading system

• UI System Integration

  • RayGUI integration for game UI
  • ImGui enhancement for editor tools
  • UI component system for ECS
  • Cross-platform UI rendering

🟡 Phase 2: AI/ML Animation Framework (Months 4-6)

High Priority - Experimental Animation Systems

• Neural Network Infrastructure

  • TensorFlow Lite C++ integration
  • ONNX Runtime integration for model inference
  • GPU acceleration with CUDA/OpenCL
  • Memory-efficient neural network execution

• PFNN (Phase-Functioned Neural Network) Implementation

  • PFNN architecture implementation
  • Locomotion synthesis system
  • Real-time character control integration
  • Terrain adaptation algorithms
  • Performance optimization for real-time usage

• DeepMimic Integration

  • Motion capture data processing pipeline
  • Physics-based character simulation
  • Reinforcement learning integration
  • Real-time motion synthesis
  • Character behavior adaptation system

• Animation Data Management

  • Motion capture data format support (BVH, FBX)
  • Animation compression algorithms
  • Temporal data interpolation
  • Animation blending with neural networks

🟢 Phase 3: AIGC Systems & Advanced AI (Months 7-9)

Medium Priority - AI Content Generation

• Procedural Content Generation

  • Neural terrain generation
  • AI-driven texture synthesis
  • Procedural animation generation
  • Level design assistance tools

• Language Model Integration

  • LLM integration for dialogue generation
  • Code generation assistance in editor
  • Natural language to animation conversion
  • AI-assisted asset creation

• Computer Vision Integration

  • Motion capture from video input
  • Real-time pose estimation
  • Facial animation from video
  • Style transfer for textures and materials

• Generative Models

  • GAN integration for texture generation
  • Variational autoencoders for animation
  • Diffusion models for asset creation
  • Neural style transfer

🔵 Phase 4: Advanced AI Features & Optimization (Months 10-12)

Low Priority - Professional AI Tools

• Advanced Character AI

  • Behavior tree integration with neural networks
  • Emotional state modeling
  • Adaptive character personalities
  • Multi-agent coordination systems

• Performance Optimization

  • Neural network quantization
  • Model pruning and compression
  • GPU memory optimization
  • Real-time inference optimization

• AI Training Pipeline

  • In-engine training data collection
  • Distributed training system
  • Transfer learning frameworks
  • Automated hyperparameter tuning

• Research Integration

  • Latest ML research paper implementations
  • Experimental animation techniques
  • Novel neural architectures
  • Academic collaboration tools

📅 Detailed AI-Focused Schedule

Quarter 1 (Months 1-3): Infrastructure

• Month 1: Custom ECS system + OGRE integration planning
• Month 2: OGRE rendering backend + Assimp enhancement
• Month 3: RayGUI integration + Asset pipeline completion

Quarter 2 (Months 4-6): Core AI Animation

• Month 4: Neural network infrastructure + TensorFlow Lite setup
• Month 5: PFNN implementation + locomotion synthesis
• Month 6: DeepMimic integration + physics-based animation

Quarter 3 (Months 7-9): AIGC Systems

• Month 7: Procedural content generation + LLM integration
• Month 8: Computer vision features + motion capture
• Month 9: Generative models + style transfer

Quarter 4 (Months 10-12): Advanced Features

• Month 10: Advanced character AI + behavior modeling
• Month 11: Performance optimization + model compression
• Month 12: Research integration + academic features

🧠 AI/ML Technology Stack

Core ML Libraries

• TensorFlow Lite: Lightweight inference engine
• ONNX Runtime: Cross-platform ML inference
• PyTorch C++: Research and experimentation
• OpenCV: Computer vision tasks
• Eigen: Linear algebra operations

Animation & Motion

• Bullet Physics: Physics simulation for DeepMimic
• Motion capture libraries: BVH, FBX parsing
• Interpolation algorithms: Spline, neural interpolation
• Character rigging: Bone hierarchy management

Rendering Integration

• OGRE 3D: Primary rendering engine
• OGRE Terrain: Procedural terrain rendering
• OGRE Particles: Particle system integration
• Custom shaders: Neural network-driven effects

🎯 AI-Specific Success Metrics

Technical Benchmarks

• Real-time PFNN inference at 60fps for 10+ characters
• DeepMimic training convergence within 24 hours
• Neural texture generation in under 5 seconds
• Motion capture to animation pipeline under 1 second latency

Feature Completeness

• Complete PFNN locomotion system
• Working DeepMimic implementation
• AI content generation tools
• Academic research integration framework

Research Goals

• Publish research papers on novel techniques
• Open-source AI animation datasets
• Collaboration with academic institutions
• Industry partnerships for validation

🚨 Critical AI Dependencies

Immediate Technical Decisions

1. ML Framework: TensorFlow Lite vs ONNX Runtime vs PyTorch C++
2. Physics Engine: Bullet Physics vs custom implementation for DeepMimic
3. ECS Architecture: Performance vs flexibility trade-offs
4. GPU Acceleration: CUDA vs OpenCL vs vendor-agnostic

Research & Data Requirements

1. Motion Capture Data: Acquisition and licensing
2. Training Datasets: Character animation, terrain, textures
3. Computational Resources: GPU clusters for training
4. Academic Partnerships: Research collaboration agreements

📈 Specialized Resource Requirements

AI/ML Development Team

• 1 ML Engineer: Neural network architecture and training
• 1 Animation Researcher: PFNN and DeepMimic expertise
• 1 Computer Vision Specialist: Motion capture and CV integration
• 1 Core Engine Developer: ECS and third-party integration
• 1 Graphics Programmer: OGRE integration and optimization

Hardware & Infrastructure

• High-end GPUs: RTX 4090 or similar for training
• Motion capture equipment: Cameras, suits, markers
• Compute cluster access: For large-scale training
• Large storage: Animation datasets and trained models

🔬 Research & Experimental Features

Cutting-Edge Animation Research

• Neural Motion Fields: Continuous motion representation
• Adversarial Motion Generation: GAN-based animation
• Physics-Informed Neural Networks: Realistic motion synthesis
• Transformer-based Animation: Attention mechanisms for motion

Novel AIGC Applications

• Procedural Narrative Generation: AI storytelling
• Dynamic Difficulty Adjustment: ML-based game balancing
• Player Behavior Prediction: Adaptive game experiences
• Real-time Asset Generation: On-demand content creation

🤝 Academic & Industry Collaboration

Research Partnerships

• Universities: Animation and ML research labs
• Game Studios: Industrial validation and feedback
• ML Conferences: SIGGRAPH, NeurIPS, ICML presentations
• Open Source Community: Collaborative development

Knowledge Sharing

• Technical Blog: Development progress and insights
• Research Papers: Novel techniques and results
• Open Datasets: Community-contributed animation data
• Educational Content: Tutorials and workshops

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Note: This engine is primarily designed for educational purposes and Windows development. While the architecture supports cross-platform development, additional work may be required for full cross-platform compatibility.