AR Video Captioning Using Base-and-Effect Frame Separation
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Solution Overview
Problem
Conventional computer vision models and video-centric MLLMs struggle to accurately disentangle and describe augmented reality (AR) effects from base video content, failing to capture the dynamic and interactive nature of AR effects across multiple frames due to their inability to isolate and describe temporal aspects, particularly when dealing with subtle alterations.
Innovation Solution
An adapted multimodal large language model (MLLM) processes concatenated frames of base and AR-enhanced video, using a hybrid vision encoder to capture semantic understanding, combined with masked prompt training, End-of-Sentence token prediction, and Low-Rank Adaptation (LoRA) for efficient fine-tuning, to generate precise captions focusing on AR effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision models and video-centric MLLMs are used to process video content, then general video understanding is achieved, but the ability to accurately disentangle and describe AR effects from base video content deteriorates
Solution Approach 1:
The patent divides the video processing task into two distinct pathways: one for processing base video content and another for processing AR effect content. By segmenting the input video into these separate streams and processing them through specialized model components, the system can accurately disentangle AR effects from the base content, resolving the contradiction between general video understanding and AR-specific accuracy.
Solution Approach 2:
The patent introduces an intermediary AR effect detection module that acts as a bridge between the base video processing and the final AR effect description. This intermediary component specifically identifies and extracts AR effect regions, allowing the model to focus computational resources on accurately describing AR content while maintaining overall video understanding capabilities.
2Loss of information
If image-centric MLLM is adapted to process multiple video frames, then temporal information extraction is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively processing only those video frames that contain AR effect changes rather than analyzing every frame in the video sequence. This approach maintains temporal information about AR effects while significantly reducing computational complexity compared to full-frame analysis.
Solution Approach 2:
The patent segments the video frame processing into base content analysis and AR effect analysis components. By dividing the processing task and only applying full temporal analysis to AR-affected regions rather than the entire video sequence, the system retains necessary temporal information while reducing overall computational complexity.
3Reliability
If conventional models fail to isolate temporal aspects of AR effects, then processing speed is maintained, but the ability to capture dynamic AR effects deteriorates
Solution Approach 1:
The patent implements periodic action by analyzing video frames at strategically selected intervals rather than continuously processing every frame. This periodic sampling approach captures the dynamic temporal aspects of AR effects at key moments while maintaining processing efficiency, resolving the contradiction between reliable dynamic effect capture and productivity.
4Measurement precision
If fine-tuning is applied to adapt MLLM for AR captioning, then caption accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent segments the fine-tuning process into targeted modules that specifically adjust only the components of the MLLM responsible for AR effect recognition and captioning. By segmenting the training task and applying fine-tuning only to relevant model portions rather than the entire model, caption accuracy is improved while minimizing training time and computational resource requirements.
Data Source
AI summary
Described herein are techniques for generating captions for augmented reality (AR) effects in videos using an adapted multimodal large language model (MLLM). The technique involves sampling frames from base and AR-applied videos, combining them into concatenated frames, and processing them with a hybrid vision encoder. Visual tokens are projected into a language model token space, reshaped, downsampled, and interleaved with text tokens. A fine-tuned large language model processes this input sequence to generate AR effect captions. Optical character recognition extracts text from AR frames, which is combined with generated captions and metadata to produce merged captions and content tags. This approach enables accurate description of temporal AR effects and facilitates downstream applications like search and ranking.