Image-Text Cross-Attention for Controllable Video Generation
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Solution Overview
Problem
Existing text-to-video diffusion models lack control over the visual appearance and geometry structure of generated videos, often relying on chance or randomness due to insufficient text-based descriptions.
Innovation Solution
A video generation model framework that conditions video output on both image and text inputs, utilizing a U-Net denoising diffusion model with multimodal video blocks and decoupled cross-attention layers to control visual features and geometric structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-only conditional control is used in video generation models, then the model complexity remains low, but the control precision over visual appearance and geometry structure deteriorates
Solution Approach 1:
The patent segments the conditioning input into multiple modalities (image and text) with dedicated processing pathways. The image encoder and text encoder process their respective inputs separately, then their features are combined through cross-attention mechanisms. This segmentation allows precise control over different aspects of video generation (visual appearance from images, semantic content from text) while maintaining modular model architecture.
Solution Approach 2:
The patent introduces cross-attention layers as intermediary components that mediate between image features and text features. These cross-attention mechanisms allow the model to selectively attend to relevant features from both modalities, enabling precise control over video generation without requiring a complete redesign of the model architecture. The cross-attention acts as a bridge that integrates multiple conditioning sources.
2Reliability
If text-only descriptions are used for video generation, then the input processing remains simple, but the generation quality and frame coherence deteriorate
Solution Approach 1:
The patent merges multiple conditioning inputs (image and text) into a unified representation that guides video generation. The image features and text features are combined through cross-attention mechanisms and integrated into the diffusion model's conditioning pathway. This merging allows the model to leverage both visual appearance information from images and semantic information from text, improving generation quality and frame coherence.
Solution Approach 2:
The patent adds an image modality dimension to the traditional text-only conditioning approach. By incorporating image inputs alongside text descriptions, the model operates in a multimodal input space that provides additional constraints and guidance for video generation. This dimensional expansion enables more precise control over visual appearance while maintaining temporal coherence through the diffusion process.
3Measurement precision
If multimodal conditioning is implemented, then control over visual appearance improves, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary processing of image and text inputs through dedicated encoders before feeding them into the diffusion model. The image encoder extracts visual features from input images, and the text encoder processes text descriptions, converting both into suitable feature representations. This preliminary action prepares the conditioning inputs in advance, allowing the main diffusion model to focus on video generation without redundant processing, thus managing computational resources more efficiently.
Solution Approach 2:
The patent applies different processing strategies to different modalities based on their specific requirements. The image encoder uses convolutional neural networks optimized for visual feature extraction, while the text encoder uses transformer-based architectures suited for language processing. The cross-attention mechanisms selectively attend to relevant features from each modality depending on the generation context. This localized optimization ensures efficient computational resource usage while maintaining high control precision.
Data Source
AI summary
Embodiments described herein provide a video generation framework built on a decoupled multimodal cross-attention module to simultaneously condition the generation on both an input image and a text input. The video generation may thus be conditioned on the visual appearance of a target object reflected in the input image. In this way, zero-shot video generation may be achieved with little fine-tuning efforts.