Video Generation Using Cross-Modal Attention for Multi-Image Animation
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Solution Overview
Problem
Current animation techniques often result in unrealistic motion due to high memory and computing resource consumption, leading to inaccurate and incomplete animated images, especially when using single still images that lack necessary details for reconstructing arbitrary poses and expressions.
Innovation Solution
A system that generates videos by selecting and combining different portions from multiple images, using a cross-modal attention module to identify and warp features from multiple source images to match a reference video, thereby improving animation accuracy and detail without explicit flow prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple images are processed to generate video frames, then animation accuracy and detail are improved, but memory consumption and computing resources increase
Solution Approach 1:
The patent extracts and processes only the necessary features from multiple source images rather than processing complete images. The cross-modal attention module selectively attends to relevant features from different images, extracting only the essential information needed for accurate animation while discarding redundant data, thus improving animation accuracy without proportionally increasing memory consumption.
Solution Approach 2:
The patent segments the animation generation process into distinct components: feature extraction from source images, cross-modal attention mechanisms for feature selection, and warping operations for feature alignment. This segmentation allows the system to process multiple images efficiently by handling them in discrete, manageable steps rather than simultaneously processing all image data at once.
2Manufacturing precision
If multiple images are processed to generate video frames, then animation accuracy and detail are improved, but computing time increases
Solution Approach 1:
The patent performs preliminary feature extraction from all source images before the actual animation generation. By pre-extracting and storing relevant features from multiple images, the system avoids redundant processing during video frame generation, significantly reducing computing time while maintaining high animation accuracy.
Solution Approach 2:
The cross-modal attention module acts as an intermediary between multiple source images and the target video frames. It efficiently selects and combines relevant features from different images through attention mechanisms, reducing the computational burden of directly processing and aligning all image data while preserving animation accuracy.
3Quantity of substance
If single still image is used for animation, then memory and computing resources are reduced, but animation accuracy and completeness deteriorate
Solution Approach 1:
The patent merges information from multiple source images by using cross-modal attention to selectively combine relevant features from each image. This combining approach allows the system to leverage the complementary information in multiple images to create complete and accurate animations, overcoming the limitations of single-image reconstruction while managing resources efficiently through selective feature integration.
Data Source
AI summary
Apparatuses, systems, and techniques to generate a video using two or more images comprising objects to be included in the video. In at least one embodiment, objects are identified in two or more images using one or more neural networks, to generate a video to include the objects in the video.