Proposal : Transforming AI-Generated Video into Object-Based Simulation for Sora to Improve video
Generating to be real .
Note : almost or all these topics were made by me !!
Subject : Enhancing Sora by Structuring Video Scenes as Dynamic, Semantic Simulations , reality
Dear OpenAI Team, im Al-Hareth Edais
I would like to propose a conceptual advancement that could significantly enhance the realism, structure, and programmability of videos generated through Sora. The idea is to shift from rendering static visual scenes to generating structured, object-oriented simulations — where every component in the video, whether physical or abstract, is treated as a programmable object with contextual logic.
Core Concept
Transform each video into a simulation-based structure, where every object, character, or element is represented by a self-contained software object. Each object holds its own properties and rules, including but not limited to:
Rich Object Profiles
Each object should include:
Visual properties: color, shape, texture, material, size
Spatial attributes: position (coordinates or relational terms like “behind the car”), orientation, and scale
State and behavior: posture and motion, current animation state, direction, velocity
Emotional or mood state: happy, angry, neutral (especially for characters)
Cognitive or logical traits: intent, awareness, interaction limits
Scene-Level Structuring
The environment (room, street, forest, sky, etc.) should be considered an object in itself, with its own properties, such as lighting, time of day, weather, topology, and layout. Relationships among objects can be expressed using absolute coordinates (x, y, z) or semantic spatial references (e.g., “left of the tree”, “under the table”).
Metadata Layer
Each video should be accompanied by a dynamic metadata stream (JSON or protocol buffers), defining all objects and their states frame-by-frame, their spatial and relational context, and their behavioral status over time. This would allow external tools, developers, or creative users to modify, analyze, or repurpose the content with ease.
Behavioral Logic Engine
Objects, especially characters or moving elements, can optionally include a lightweight logic engine, enabling reactions to other objects, internal consistency ( a sad person doesn’t smile, or a ball doesn’t float unless supported), and semi-autonomous behavior for interaction or simulation expansion.
Export and Integration Capabilities
This object-based structure would allow generated videos to be exported to game engines such as Unity or Unreal, as well as VR/AR applications, robotics simulation environments, and scientific or educational modeling tools.
Semi-Interactive Control
Providing users with an interface or scripting layer to modify object properties (before or after generation) would turn Sora into a hybrid between a video generator and an intelligent scene designer.
Simulation Testing and Variability
This structure allows for generating multiple variations of the same scene by tweaking specific object properties or states — valuable for testing, content diversity, or dataset generation.
Benefits
Enhanced realism and logical consistency across frames
Higher creative control for developers and creators
Interoperability with 3D engines, simulation tools, and vR platforms
New use cases such as training data generation, interactive storytelling, and educational simulation
This proposal aims to align Sora’s future with the growing demand for interactive, intelligent, and context-aware visual content. I believe this direction could set a new benchmark in generative video technology.
My Name :-
[Al_Hareth_Edais]
Email:-
edahareth@gmail.com
Transforming AI Video Generation into Object-Based Semantic Simulation
Concept Overview
Key Innovations
Applications
Vision