Diffusion Video Transition Generation with Latent Frame Anchors
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Solution Overview
Problem
Existing methods for generating high-quality transition videos struggle with maintaining semantic consistency, high fidelity, smoothness, and alignment with text prompts, often relying on self-collected datasets and requiring time-consuming training on large-scale motion video datasets.
Innovation Solution
A method using a pre-trained image-to-video diffusion model generates transition frames by creating latent noises based on start and end frames and captions, leveraging latent space representations and de-noising techniques to improve smoothness and reduce randomness without additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing methods are used to generate transition videos, then the videos can be created, but semantic consistency and fidelity are poor
Solution Approach 1:
The method performs preliminary encoding of start and end frames into latent representations before generating transition frames. This preliminary action establishes semantic anchors that guide the diffusion process, ensuring semantic consistency and fidelity throughout the transition while maintaining smoothness and alignment with text prompts.
Solution Approach 2:
The patent introduces latent representations as an intermediary between the input frames and the diffusion model. These latent codes act as mediators that preserve semantic information from start and end frames, enabling the model to generate transitions with improved semantic consistency, fidelity, smoothness, and text alignment without compromising reliability.
2Manufacturing precision
If self-collected datasets and large-scale motion video datasets are used for training, then model performance can be improved, but training time and computational resources increase significantly
Solution Approach 1:
The method performs preliminary encoding of start and end frames into latent representations before generating transition frames. This preliminary action establishes semantic anchors that guide the diffusion process, ensuring semantic consistency and fidelity throughout the transition while maintaining smoothness and alignment with text prompts.
Solution Approach 2:
The system uses the pre-trained diffusion model's existing capabilities to generate transition frames directly, without requiring additional training on self-collected or large-scale motion video datasets. The model serves itself by leveraging its pre-trained understanding of video dynamics and diffusion processes, eliminating time-consuming training steps while maintaining high transition quality.
3Manufacturing precision
If additional training is performed to improve transition quality, then semantic consistency may improve, but the process becomes more complex and time-consuming
Solution Approach 1:
The patent introduces latent representations as an intermediary between the input frames and the diffusion model. These latent codes act as mediators that preserve semantic information from start and end frames, enabling the model to generate transitions with improved semantic consistency, fidelity, smoothness, and text alignment without compromising reliability.
Solution Approach 2:
The system uses the pre-trained diffusion model's existing capabilities to generate transition frames directly, without requiring additional training on self-collected or large-scale motion video datasets. The model serves itself by leveraging its pre-trained understanding of video dynamics and diffusion processes, eliminating time-consuming training steps while maintaining high transition quality.
Data Source
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AI summary
Implementations of the present disclosure provide a method, device, and medium for generating transition videos with a diffusion model. The method comprises obtaining a start frame (112) and an end frame (122) for the video (132), a first caption (114) of the start frame, and a second caption (124) of the end frame. The method further comprises generating a first latent noise (116) in a latent space based on the start frame and a second latent noise (124) in the latent space based on the end frame. The method further comprises generating third latent noises (128) in the latent space corresponding to transition frames (130) based on the first latent noise and the second latent noise. In addition, the method further comprises generating, by utilizing a pre-trained image-to-video diffusion model (104), the transition frames based on the third latent noises, the start frame, the end frame, the first caption, and the second caption.