Multimodal Video Generation With Decoupled Cross-Attention Control
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Solution Overview
Problem
Existing video generation models lack control over the visual appearance and geometry structure, relying heavily on randomness and chance due to insufficient text-based conditional control.
Innovation Solution
A video generation model (VDM) 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 enhance control over 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 operation remains simple, but the control precision over visual appearance and geometry structure deteriorates
Solution Approach 1:
The patent segments the conditioning input into two distinct modalities: image conditions and text conditions. The image encoder processes visual appearance and geometry information separately from the text encoder, which handles descriptive conditions. This segmentation allows each modality to contribute specialized control signals to the video generation process, improving overall control precision without requiring the entire model to handle all types of control simultaneously.
Solution Approach 2:
The patent adds an image dimension to the traditional text-only conditioning approach. By incorporating image inputs alongside text prompts, the system transitions from one-dimensional text control to multi-dimensional control (image + text). This dimensional expansion enables precise control over visual appearance through image conditions while maintaining descriptive control through text, thereby improving control precision without proportionally increasing complexity.
2Manufacturing precision
If multimodal conditioning with image and text inputs is implemented, then video generation quality and control precision improve, but the computational complexity and processing time increase
Solution Approach 1:
The patent employs pre-trained image and text encoders that have already learned robust feature representations from large-scale datasets. These pre-trained encoders can quickly extract meaningful conditions from input images and text prompts without requiring extensive processing during video generation. This preliminary learning phase, completed during training, reduces the computational burden and processing time during actual video generation while maintaining high generation quality.
Solution Approach 2:
The patent introduces encoder modules as intermediary components between the raw inputs (images and text) and the video generation model. These encoders act as mediators that transform diverse inputs into standardized condition representations that the diffusion model can efficiently process. This intermediary layer simplifies the integration of multimodal inputs and reduces the computational complexity that would otherwise arise from directly processing raw images and text within the generation model.
3Measurement precision
If decoupled cross-attention layers are used to process image and text conditions separately, then control over specific visual features improves, but the model architecture complexity increases
Solution Approach 1:
The patent implements decoupled cross-attention layers that separately process image conditions and text conditions through distinct attention mechanisms. The image cross-attention layer focuses on visual feature alignment while the text cross-attention layer handles descriptive condition integration. This segmentation of attention processing allows each layer to specialize in specific types of control, improving feature control precision while keeping the architecture organized and manageable through clear functional separation.
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.