CineRenderAI Specification

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CineRenderAI

Real-time neural rendering technology.

CineRenderAI is an open-source real-time neural rendering engine that uses AI to transform low-cost rasterized graphics into cinematic-quality visuals with enhanced lighting, materials, detail, and temporal stability.

The project is designed as a modular, hardware-agnostic rendering platform that can integrate with existing game engines and graphics pipelines. CineRenderAI focuses on neural reconstruction rather than simply increasing the computational cost of traditional rendering.

Vision

CineRenderAI aims to bridge the gap between real-time rendering and cinematic visual fidelity.

The long-term vision is to enable game engines and interactive applications to render scene structure efficiently while neural models reconstruct increasingly sophisticated visual information, including lighting, materials, reflections, atmospheric effects, and fine surface detail.

The project is intended to provide an open-source foundation for experimentation and development in real-time neural rendering.

Goals

Develop a real-time neural rendering pipeline
Reduce the computational cost of high-fidelity graphics
Improve visual fidelity through AI reconstruction
Maintain temporal stability across frames
Provide hardware-agnostic GPU acceleration
Support integration with existing game engines
Develop open training and evaluation methodologies
Enable community-driven neural rendering research
Provide modular components that can evolve independently

Core Modules

Frame Reconstruction Module

The Frame Reconstruction Module provides the primary neural image reconstruction pipeline.

It processes rendering information such as:

Low-resolution color frames
Depth data
Motion vectors
Surface normals
Material information
Previous frame information
Rendering metadata

The module reconstructs higher-fidelity frames while preserving important scene structure and visual information.

Core capabilities include:

Neural super resolution
Detail reconstruction
Edge reconstruction
Texture enhancement
Anti-aliasing
Fine detail synthesis
Image quality preservation

Temporal Intelligence Module

The Temporal Intelligence Module provides persistent understanding across consecutive frames.

Rather than treating every frame as an independent image, the module analyzes temporal relationships between frames to maintain visual consistency.

Core capabilities include:

Temporal feature tracking
Motion-aware reconstruction
Frame history analysis
Temporal artifact reduction
Ghosting reduction
Flicker reduction
Detail persistence
Scene consistency

The module is designed to establish a foundation for longer-term scene memory as the system evolves.

Neural Lighting Module

The Neural Lighting Module reconstructs advanced lighting effects using AI-assisted inference.

Core capabilities include:

Global illumination reconstruction
Indirect lighting
Light bounce estimation
Soft shadow reconstruction
Ambient lighting
Atmospheric lighting
Light transport approximation
Dynamic lighting enhancement

The module is intended to reduce the computational burden associated with expensive lighting techniques while maintaining visual quality.

Neural Reflection Module

The Neural Reflection Module reconstructs reflective surfaces and view-dependent lighting information.

Core capabilities include:

Screen-space reflection enhancement
Reflection reconstruction
Missing reflection information
View-dependent surface response
Dynamic reflection approximation
Temporal reflection stability

The module can use scene geometry, depth, normals, materials, and temporal information to reconstruct reflections that are unavailable from the current frame alone.

Material Intelligence Module

The Material Intelligence Module enhances the visual representation of physical surfaces.

Core capabilities include:

Material classification
Surface property reconstruction
Roughness enhancement
Specular response
Micro-detail reconstruction
Texture refinement
Material consistency across frames

The module is designed to allow relatively simple assets to produce richer visual results through neural inference.

Scene Understanding Module

The Scene Understanding Module provides structured interpretation of the rendered scene.

It can analyze:

Object identity
Surface relationships
Scene geometry
Camera position
Object motion
Material properties
Lighting relationships
Depth relationships

Scene understanding provides contextual information to other CineRenderAI modules and establishes a foundation for future neural world representations.

Neural Detail Module

The Neural Detail Module generates additional visual information that may not be present in the source render.

Core capabilities include:

Texture detail enhancement
Surface microstructure
Fine geometric appearance
Texture restoration
Procedural detail reconstruction
Asset enhancement

The module should prioritize temporal consistency and source fidelity rather than unconstrained image generation.

Motion Intelligence Module

The Motion Intelligence Module analyzes object and camera movement to improve reconstruction and frame generation.

Core capabilities include:

Motion vector analysis
Object motion estimation
Camera motion estimation
Motion-aware detail preservation
Temporal prediction
Motion artifact reduction

The module provides motion information to the Temporal Intelligence and Frame Reconstruction modules.

Inference Optimization Module

The Inference Optimization Module is responsible for making neural rendering practical for real-time workloads.

Core capabilities include:

Model quantization
Model pruning
Tensor optimization
Tile-based inference
Adaptive inference resolution
Resource scheduling
Frame reuse
Region prioritization
GPU memory optimization

The module should allow quality and performance to be adjusted independently according to available hardware.

GPU Compute Module

The GPU Compute Module provides the hardware acceleration layer for neural rendering.

The architecture should prioritize vendor-neutral standards and provide abstraction between CineRenderAI and individual GPU implementations.

Potential backends include:

Vulkan
OpenCL
CUDA
ROCm
Other compatible compute backends

Vendor-specific acceleration should remain optional and should not be required by the core architecture.

Pipeline Orchestration Module

The Pipeline Orchestration Module coordinates the individual neural rendering components.

It manages:

Module execution order
Resource allocation
Frame dependencies
Temporal state
Model selection
Quality settings
Performance targets
Plugin execution
Error handling

The orchestration system should allow individual modules to be replaced, upgraded, disabled, or extended without requiring the entire rendering pipeline to be redesigned.

Model Management Module

The Model Management Module manages neural models used throughout CineRenderAI.

Core capabilities include:

Model registration
Model loading
Model versioning
Model selection
Hardware-aware model selection
Model caching
Model validation
Runtime model switching

Models should be treated as replaceable components rather than permanently embedded into the rendering engine.

Benchmarking Module

The Benchmarking Module provides standardized evaluation of neural rendering quality and performance.

Metrics may include:

Frame time
Frames per second
GPU utilization
Memory consumption
Reconstruction quality
Temporal stability
Artifact frequency
Lighting accuracy
Material fidelity
Reflection quality

The benchmarking system should allow CineRenderAI results to be compared against native rendering and other reconstruction technologies using consistent methodologies.

Optional Plugin Modules

CineRenderAI should support optional plugins that extend functionality without expanding the mandatory core.

Game Engine Plugins

Optional integrations may provide support for:

Godot
Unreal Engine
Unity
Other compatible engines

Engine plugins should translate native engine rendering information into CineRenderAI-compatible inputs.

Advanced Frame Generation Plugin

An optional frame generation system may generate intermediate frames using temporal and motion information.

The plugin should prioritize:

Low latency
Motion consistency
Object boundary accuracy
Temporal stability
User-configurable quality

Neural Path Tracing Plugin

An optional plugin may use neural models to approximate selected path tracing workloads.

Potential capabilities include:

Neural global illumination
Neural ray reconstruction
Neural denoising
Indirect lighting reconstruction
Reflection reconstruction

Atmospheric Rendering Plugin

An optional atmospheric rendering module may enhance:

Fog
Volumetric lighting
Clouds
Smoke
Dust
Haze
Atmospheric scattering

Neural Texture Plugin

An optional plugin may enhance existing textures through AI-assisted reconstruction.

Potential capabilities include:

Texture upscaling
Texture restoration
Material-aware detail
Surface aging
Procedural detail synthesis

Cinematic Effects Plugin

An optional plugin may provide neural or AI-assisted cinematic effects such as:

Depth of field
Motion blur
Lens effects
Volumetric effects
Filmic image reconstruction
Advanced tone mapping

Legacy Game Enhancement Plugin

An optional plugin may provide compatibility with older rendering pipelines and games.

The goal is to allow older titles to benefit from modern neural reconstruction without requiring the original game engine to be rewritten.

Virtual Reality Plugin

An optional VR integration may provide specialized neural rendering for stereoscopic and immersive environments.

Potential capabilities include:

Per-eye reconstruction
Foveated neural rendering
Motion prediction
Latency optimization
Stereo consistency

Developer Tools Plugin

Optional developer tools may provide:

Frame inspection
Neural model visualization
Temporal history visualization
Performance profiling
Artifact detection
Quality comparison
Model debugging

Engine Integration

CineRenderAI should integrate with game engines through clearly defined interfaces.

An integration should provide the renderer with relevant scene information while allowing the original engine to retain control over gameplay, physics, animation, and scene management.

The integration architecture should support:

Render target access
Depth access
Motion vector access
Normal data
Material information
Camera information
Object identifiers
Lighting information
Frame history

Training System

CineRenderAI should include an open training framework for developing and evaluating neural rendering models.

The training system should support paired and multi-modal rendering data.

Potential training inputs include:

Rasterized renders
Path-traced renders
Depth
Normals
Motion vectors
Material data
Lighting information
Camera information

Training targets may include:

High-fidelity reference frames
Lighting solutions
Reflection solutions
Material responses
Temporal reconstruction targets

The training system should support reproducible experiments and clearly documented datasets.

Open Rendering Dataset

The project should encourage development of an open neural rendering dataset.

The dataset should prioritize legally distributable and appropriately licensed content.

Dataset categories may include:

Indoor environments
Outdoor environments
Urban environments
Natural environments
Vehicles
Characters
Materials
Weather
Atmospheric conditions
Dynamic lighting
Complex motion

Each dataset entry should preserve sufficient metadata to reproduce or evaluate the associated rendering scenario.

Hardware Compatibility

CineRenderAI should prioritize broad hardware compatibility.

The core system should avoid requiring a specific GPU vendor or proprietary accelerator.

Hardware-specific optimizations may be implemented through optional backends when they provide measurable performance improvements without compromising the portability of the core system.

Quality and Performance Modes

CineRenderAI should support configurable rendering profiles.

Potential profiles include:

Performance
Balanced
Quality
Cinematic
Maximum Fidelity

Profiles may control:

Internal render resolution
Neural model selection
Temporal history
Lighting reconstruction
Material enhancement
Reflection quality
Inference precision
Frame generation
Plugin execution

Developer API

CineRenderAI should provide a modular API allowing applications and plugins to interact with the rendering pipeline.

The API should provide controlled access to:

Frame data
Scene metadata
Temporal state
Neural models
Rendering modules
Performance information
Configuration
Plugin interfaces

The API should remain stable where possible while allowing internal rendering technologies to evolve.

Open Research

CineRenderAI is intended to serve as a platform for experimentation in:

Neural rendering
Real-time reconstruction
Neural lighting
Neural materials
Temporal scene understanding
Neural frame generation
GPU inference
Real-time path tracing approximation
AI-assisted graphics

Experimental features should be able to exist independently from stable core functionality.

Security and Reliability

CineRenderAI should prioritize predictable and controlled execution.

The system should provide:

Model validation
Plugin isolation where practical
Resource limits
Error recovery
Graceful fallback to conventional rendering
Deterministic evaluation modes where practical

A neural rendering failure should not prevent an application from continuing to render through its underlying graphics pipeline whenever a fallback is available.

Fallback Rendering

CineRenderAI should never require neural rendering for an application to function.

If a compatible neural model, GPU backend, or plugin is unavailable, the system should be capable of falling back to the application’s standard rendering pipeline.

This ensures that neural enhancement remains an optional acceleration and fidelity layer rather than a mandatory dependency.

Future Direction

The long-term objective of CineRenderAI is to evolve from neural frame reconstruction toward neural scene rendering.

Future systems may use structured scene information to reconstruct increasingly sophisticated representations of:

Geometry
Materials
Lighting
Reflections
Atmosphere
Motion
Camera perspective

The ultimate goal is to move from reconstructing individual frames toward understanding and rendering complete interactive worlds.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution:
- Roxanne Ardary
- https://www.roxanneardary.com/

Optional

Specification Branding License (SBL)
- Attribution-free commercial deployment
- Pricing based on scale, usage, and deployment scope
- https://roxanneardary.com/cinerenderai/

License & Notice Requirements

CineRenderAI is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).
By contributing to this project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to:
Roxanne Ardary and roxanneardary.com.
CineRenderAI specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments. Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

Notice – CineRenderAI

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – March 16, 2026
Created the repository for CineRenderAI. Developed the open-source AI platform for real-time neural rendering, including neural model design, temporal frame reconstruction, AI lighting, and engine architecture.
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – CineRenderAI

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points:

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License.