System and method for efficient scene continuity in visual and multimedia using generative artificial intelligence

Patent metadata

Field	Value
Publication number	US-12499515-B2
Application number	US-202418736517-A
Country	US
Kind code	B2
Filing date	Jun 6, 2024
Priority date	Jun 6, 2024
Publication date	Dec 16, 2025
Grant date	Dec 16, 2025

Abstract

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

First claim

What is claimed is:

1 . A computing system for managing scene continuity in generated or augmented visual media, the computing system comprising:
one or more hardware processors configured for:
receiving a content specification request associated with a scene for continuity-aware content management;
selecting one or more generative models based on the content specification, wherein the selecting comprises analyzing scene-specific continuity requirements including object positioning, lighting conditions, and camera perspective across multiple scenes;
preprocessing data based on the content specification to prepare the data for the selected generative models, wherein the preprocessing comprises:
cleaning multi-modal visual data while maintaining temporal and spatial relationships between frames;
identifying and tracking specific objects across multiple scenes to ensure consistent object representation;
mapping lighting patterns and camera angles across scene transitions; and
transforming the data into a format that preserves visual and narrative continuity markers;

selecting, training, fine-tuning, or augmenting the selected generative models using the preprocessed data, wherein the training comprises:
implementing an adversarial network architecture with a generator network and a discriminator network;
training the discriminator to identify visual discontinuities between scenes; and
optimizing the generator to produce content that maintains consistent representation of characters, environments, narrative elements, and visual assets across multiple clips or scenes;

generating scene continuity-aware content using the selected generative models based on the content specification request, wherein the generating comprises:
frame interpolation and view synthesis to create transitions between scenes that maintain continuity of subject appearance, lighting, and scene geometry;
perspective reconfiguration based on user-defined camera motion and angle inputs;
generative scene extension through outpainting or recomposition to fill occluded or non-visible spatial regions beyond original frame boundaries; and
synthesizing synchronized audio, including ambient environmental sounds and character dialogue, based on the visual content and content specification; and

outputting the generated scene continuity-aware content artifacts or representations through a three-dimensional rendering engine that applies consistent shading, texture mapping, and perspective transformations across scene boundaries.

2 . The computing system of claim 1 , wherein the user content specification comprises one or more design elements, user preference configuration documents, or templates associated with scene continuity generation.

3 . The computing system of claim 1 , wherein selecting one or more generative models comprises selecting a generator network and a discriminator network.

4 . The computing system of claim 3 , wherein training the selected generative models comprises training the generator network and the discriminator network adversarially using the preprocessed data.

5 . The computing system of claim 1 , wherein generating scene continuity content comprises:
engineering one or more prompts for the trained generative models based on the user content specification; and submitting one or more prompts as input to the trained generative models to generate the scene continuity content.

6 . The computing system of claim 5 , wherein one or more prompts include desired camera angles, temporal positions, transitions, or other scene-specific attributes.

7 . The computing system of claim 1 , further comprising:
selecting a frame interpolation and view synthesis subsystem based on the content specification; and applying the frame interpolation and view synthesis module to the generated scene continuity content to create smooth transitions and novel viewpoints.

8 . The computing system of claim 1 , wherein training the selected generative models comprises:
initializing the selected generative models with predefined architectures and hyperparameters; iteratively updating the model parameters using optimization algorithms; and monitoring and evaluating the training progress using metrics and validation techniques.

9 . The computing system of claim 1 , wherein outputting the generated scene continuity content comprises:
applying post-processing techniques to enhance the visual quality and realism of the generated content; and providing the generated content in a format compatible with the user specification or downstream applications.

10 . The computing system of claim 1 , wherein generating scene continuity-aware content further comprises generating synchronized audio including ambient environmental sounds or character dialogue aligned with the generated visual frames.

11 . A computer-implemented method, the computer-implemented method comprising:
receiving a content specification request associated with a scene for continuity-aware content management; selecting one or more generative models based on the content specification, wherein the selecting comprises analyzing scene-specific continuity requirements including object positioning, lighting conditions, and camera perspective across multiple scenes; preprocessing data based on the content specification to prepare the data for the selected or generative models, wherein the preprocessing comprises:
cleaning multi-modal visual data while maintaining temporal and spatial relationships between frames;
identifying and tracking specific objects across multiple scenes to ensure consistent object representation;
mapping lighting patterns and camera angles across scene transitions; and
transforming the data into a format that preserves visual and narrative continuity markers;
selecting, training, fine-tuning, or augmenting the selected generative models using the preprocessed data, wherein the training comprises:
implementing an adversarial network architecture with a generator network and a discriminator network;
training the discriminator to identify visual discontinuities between scenes; and
optimizing the generator to produce content that maintains consistent representation of characters, environments, narrative elements, and visual assets across multiple clips or scenes;
generating scene continuity-aware content using the selected generative models based on the content specification request, wherein the generating comprises:
frame interpolation and view synthesis to create transitions between scenes that maintain continuity of subject appearance, lighting, and scene geometry;
perspective reconfiguration based on user-defined camera motion and angle inputs;
generative scene extension through outpainting or recomposition to fill occluded or non-visible spatial regions beyond original frame boundaries; and
synthesizing synchronized audio, including ambient environmental sounds and character dialogue, based on the visual content and content specification; and
outputting the generated scene continuity—aware content artifacts or representations through a three-dimensional rendering engine that applies consistent shading, texture mapping, and perspective transformations across scene boundaries.

12 . The computer-implemented method of claim 11 , wherein the content specification comprises one or more design elements, user preference configuration documents, or templates associated with scene continuity generation.

13 . The computer-implemented method of claim 11 , wherein selecting one or more generative models comprises selecting a generator network and a discriminat

Assignees

• Qomplx Llc

Inventors

• Crabtree Jason
• Kelley Richard
• Hopper Jason
• Park David

Classifications

• G06T5/60 Primary

  using machine learning, e.g. neural networks · CPC title