Diffusion-Based Virtual Object Placement for Realistic Video Scenes
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Solution Overview
Problem
Existing techniques for virtual object placement in video sequences are time-consuming, require significant manual skill, and often result in unnatural appearances due to inadequate consideration of environmental conditions and lighting, lacking user interaction and automation.
Innovation Solution
An end-to-end approach using generative artificial intelligence models, including a foundation diffusion model and adapters, to automatically identify suitable placement locations and adjust the appearance of virtual objects based on perspective and lighting information, ensuring realistic integration into the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual manipulation methods (rotoscoping) are used for virtual object placement, then placement precision and control are improved, but time consumption and skill requirement increase significantly
Solution Approach 1:
The system performs automatic object extraction and placement without requiring manual rotoscoping. The deep learning model autonomously processes the entire workflow from source video to final composite video, eliminating the need for manual tracing and matte creation while maintaining placement precision.
Solution Approach 2:
The patent replaces manual mechanical manipulation (rotoscoping tools, frame-by-frame tracing) with an automated neural network system that uses deep learning to extract objects and generate placement results, substituting human skill with computational intelligence.
2Loss of time
If simple automated object extraction is used, then time consumption is reduced, but the ability to account for lighting and atmospheric differences deteriorates
Solution Approach 1:
The system dynamically adjusts multiple parameters including lighting conditions, atmospheric effects, camera movement, and object properties based on the source video analysis. The neural network modifies these parameters to ensure the extracted object blends seamlessly with the destination scene, maintaining visual consistency while operating automatically.
3Manufacturing precision
If trial-and-error approach with multiple iterations is used, then placement accuracy can be improved through human evaluation, but productivity and efficiency deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the neural network analyzes the source video, extracts objects, and generates placement results that are automatically evaluated for consistency. The system iterates internally to optimize placement accuracy without requiring multiple manual trials, achieving both high accuracy and efficiency.
Data Source
AI summary
Embodiments of the present disclosure provide techniques for performing virtual object placement in a video sequence using generative artificial intelligence models. An example method generally includes receiving an input prompt specifying an object to insert into a scene depicted in an input image stream; decoding, using a generative artificial intelligence model, perspective and lighting information for the input image stream; determining, based on the decoded perspective and lighting information, a location in the scene in which the object is to be inserted; and generating, using the generative artificial intelligence model, an output image stream including the object into the scene at the determined location, wherein visual effects for the object are based on the perspective and lighting information for the input image stream.