Subject-Aware Video Compositing With Motion-Aligned Backgrounds

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Solution Overview

Problem

Conventional video compositing techniques require numerous manual interactions, leading to increased computational resource consumption, reduced user efficiency, and limited flexibility in iterating different background ideas, while struggling to generate backgrounds that align with the motion and appearance of foreground subjects and environments.

Innovation Solution

A video compositing service utilizing a machine-learning model with cross-frame attention for temporal reasoning, generating composite videos that align the movement of a subject with a condition frame's environment, incorporating realistic foreground-background interactions using a diffusion-based model trained on large-scale human-scene interaction datasets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional video compositing techniques are used, then background generation is achieved, but computational resource consumption increases and user efficiency decreases due to numerous manual interactions

Engineering Contradiction:

Improvevideo editing timeVSAvoidmanual interactions

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system enables automatic background generation by having the machine-learning model process the input video and condition frame autonomously to generate the composite video, eliminating the need for numerous manual interactions and significantly reducing video editing time while maintaining high productivity

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical operations with an automated machine-learning-based system that uses diffusion models and cross-frame attention mechanisms to generate backgrounds automatically, substituting human manual editing with intelligent automated processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If conventional video compositing techniques are used, then background generation is achieved, but flexibility in iterating different background ideas is limited

Engineering Contradiction:

Improvebackground iteration flexibilityVSAvoidmanual adjustment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system provides dynamic adaptability by allowing users to easily iterate through different background ideas by simply providing different condition frames, with the machine-learning model automatically adapting the background generation to match each new condition without requiring complex manual adjustments

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent enables flexible background iteration by changing input parameters (condition frames) and having the diffusion model generate corresponding background variations, allowing users to explore multiple background ideas by simply modifying input conditions rather than performing complex manual adjustments

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If conventional video compositing techniques are used, then background generation is achieved, but the background does not align well with subject motion and appearance

Engineering Contradiction:

Improveforeground-background alignmentVSAvoidbackground alignment consistency

Core Design Contradiction:

Manufacturing precisionVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms through cross-frame attention that continuously monitors and adjusts the background generation based on subject motion and appearance in the input video, ensuring consistent and precise alignment between the foreground subject and generated background throughout the video sequence

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies preliminary action by analyzing the input video to extract subject motion patterns and appearance features before generating the background, allowing the diffusion model to pre-plan background elements that will align consistently with the subject's movements throughout the video

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260038126A1Subject-aware video background generation

Publication Date: 2026.02.05 ADOBE INC

AI summary

In one implementation of subject-aware background video generation, a processing device generates mask data and foreground feature data from frames of a subject video. The mask data separates a subject depicted in the subject video from an environment therein. The foreground feature data describes the features of the subject. The processing device receives a condition frame that depicts a different environment. A machine-learning model generates a composite video by aligning the subject's movement with the different environment from inputs of the foreground feature data, the mask data, and the condition frame, which conditions the generation of the different environment for the composite video. The processing device then presents the composite video via a user interface.