Text-Based Video Editing Model with Cloud-Edge Separation
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Solution Overview
Problem
Conventional video editing approaches on edge devices face computational resource limitations, preventing on-device learning and backpropagation, and struggle with temporal inconsistency and structural guidance in text-to-video models.
Innovation Solution
A processor-implemented method that extracts features from a video input, injects noise conditioned on the input, and processes it using an artificial neural network (ANN) to adapt the video based on a text prompt, enhancing temporal consistency and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video editing approaches use fine-tuning or test-time training on the input video, then the video editing quality is improved, but the computational resource requirements increase beyond edge device capacity
Solution Approach 1:
The patent extracts the learning capability from the edge device and relocates it to the cloud server. The video editing model performs fine-tuning and test-time training on the cloud server with sufficient computational resources, then deploys the trained model to the edge device for inference only. This separation allows high-quality video editing without requiring edge devices to have the computational capacity for on-device learning.
Solution Approach 2:
The patent introduces a cloud server as an intermediary between the video editing model and the edge device. The cloud server handles the computationally intensive fine-tuning and training operations, while the edge device only performs lightweight inferencing. This intermediary architecture enables edge devices to access advanced video editing capabilities without needing to possess the required computational resources locally.
2Adaptability or versatility
If text-to-video models are used for video editing, then the adaptability to text prompts is improved, but the temporal consistency of the generated video deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the model processes video frames sequentially, using information from previously processed frames to guide the generation of current frames. The model incorporates temporal attention mechanisms that allow it to reference and maintain consistency with earlier frames while still responding to text prompts, thereby preserving temporal coherence throughout the video sequence.
Solution Approach 2:
The patent employs dynamic processing where the model adapts its behavior based on the temporal position in the video sequence. Different frames are processed with varying degrees of attention to temporal context, allowing the model to maintain stability in static regions while enabling flexibility for text-driven changes. This dynamic approach balances temporal consistency with text prompt adaptability.
Data Source
AI summary
A processor-implemented method performed for text-based video editing includes receiving a video input and a text prompt. The video input includes a sequence of video frames. Features of the video input are extracted to generate a latent representation of the video input. Noise is injected to the latent representation of the video input to generate a noise injected latent. The noise is conditioned on the video input. An artificial neural network (ANN) model processes the noise injected latent based on the text prompt to adapt the video input according to the text prompt.