Video Diffusion Editing With Fixed Temporal Attention

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Solution Overview

Problem

Existing video editing technologies struggle to efficiently generate high-quality videos with customized object motion and appearance edits that maintain temporal consistency, particularly when using video diffusion models.

Innovation Solution

A conditional video generation system that fine-tunes a video diffusion model by optimizing a mixed fine-tuning objective, incorporating a degraded version of the input video and applying a sequence of reverse diffusion steps to preserve spatiotemporal information, while updating the model's spatial attention layers while keeping temporal attention layers fixed.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If video diffusion models are used for text-based appearance or motion editing, then customization of object motion and appearance is improved, but temporal consistency deteriorates

Engineering Contradiction:

Improvecustomization of object motion and appearanceVSAvoidtemporal consistency

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The patent segments the video diffusion model into separate spatial and temporal attention layers. By independently optimizing the spatial attention layers while keeping temporal attention layers fixed, the system enables customized appearance and motion edits (improving adaptability) while preserving temporal consistency (stability of object composition). This segmentation allows different parts of the model to serve different functions without interfering with each other.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by making different parts of the model have different properties - specifically, making spatial attention layers learnable and adjustable while keeping temporal attention layers fixed. This allows the model to adapt to local customization requirements (appearance/motion edits) while maintaining global temporal consistency through the fixed temporal attention mechanisms.

Inventive Principle:

Principle #3Local quality

2Manufacturing precision

If spatial attention layers are optimized while keeping temporal attention layers fixed, then manufacturing precision of visual output is improved, but device complexity increases

Engineering Contradiction:

Improvevisual output qualityVSAvoidmodel training complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent extracts and separates the spatial attention layers from the temporal attention layers, allowing independent optimization of only the spatial components. This extraction simplifies the training process compared to optimizing the entire model, as only the spatial attention layers need to be updated during fine-tuning, reducing the effective device complexity while maintaining high visual output quality.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of optimizing the entire video diffusion model (excessive action), the patent applies partial action by optimizing only the spatial attention layers while keeping temporal attention layers fixed. This partial optimization approach achieves sufficient visual output quality (manufacturing precision) without the computational burden of training the complete model from scratch or optimizing all parameters.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If reverse diffusion steps are applied to preserve spatiotemporal information, then reliability of temporal coherence is improved, but loss of time increases

Engineering Contradiction:

Improvetemporal coherenceVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-freezing the temporal attention layers before fine-tuning the spatial attention layers. This preliminary configuration ensures that temporal coherence is preserved during the generation process, as the fixed temporal attention mechanisms maintain consistent temporal relationships. The reverse diffusion steps then operate on this pre-configured foundation, achieving reliable temporal coherence without requiring extensive computation time to learn temporal patterns from scratch.

Inventive Principle:

Principle #10Preliminary action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables the generation of high-quality videos with smooth visual modifications that align with temporal information, preserving object identity and maintaining temporal coherence, facilitating applications like animation and dynamic camera motion.

Implementation Method 1

processing, by a diffusion model, a diffusion model input comprising (i) a current intermediate representation of the output video and (ii) the input text to generate a noise output for the step; and using the noise output to de-noise the current intermediate representation of the output video

Methodology Applied

Scientific EffectDiffusion: Diffusion

Data Source

PatentUS20260087701A1Video editing using diffusion models

Publication Date: 2026.03.26 GOOGLE LLC

AI summary

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output video. One of the methods include: obtaining an input video; obtaining input text that includes a description of an output video; generating, based at least on applying downsampling to the input video, a degraded version of the input video; and generating the output video based on the description in the input text by updating the degraded version of the input video by using a video diffusion model across a plurality of reverse diffusion steps.