Diffusion Video Transition Generation with Latent Noise Interpolation
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Solution Overview
Problem
Existing methods for generating high-quality transition videos face challenges in achieving semantic consistency, high fidelity, smoothness, and alignment with text prompts, often relying on self-collected datasets and requiring time-consuming training, leading to discontinuous and abrupt transitions.
Innovation Solution
A method involving a pre-trained image-to-video diffusion model that generates transition frames by leveraging latent noises derived from start and end frames, using latent noise interpolation and low-rank adaption, and text embeddings to improve smoothness and alignment without additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to generate transition videos, then the generation process can be performed, but the transitions are discontinuous and abrupt, lacking smoothness
Solution Approach 1:
The method performs preliminary encoding of start and end frames into latent representations before generating transition frames. This pre-processing establishes a structured latent space that guides the diffusion model to produce smooth transitions while maintaining fidelity to the original frames.
Solution Approach 2:
The patent introduces latent representations as an intermediary between the input frames and the diffusion model. These latent codes serve as a bridge that captures essential features while enabling smooth interpolation, resolving the contradiction between smoothness and fidelity.
2Adaptability or versatility
If self-collected datasets and training are used, then model customization is achieved, but the process is time-consuming
Solution Approach 1:
The method copies the latent encoding architecture from pre-trained models and applies it to new frame pairs without full retraining. This allows customization for different video transitions while avoiding the time-consuming training process, achieving both adaptability and efficiency.
Solution Approach 2:
The patent utilizes pre-trained models that have already learned general video representation patterns. This preliminary training phase is performed once, and the learned representations can be applied to multiple transition video generation tasks, reducing repeated training time while maintaining adaptability.
3Manufacturing precision
If diffusion models are used for video generation, then high quality output is achieved, but semantic consistency and text prompt alignment are difficult to maintain
Solution Approach 1:
The method incorporates feedback mechanisms where the latent representations of start and end frames guide the diffusion process. This feedback ensures that generated transition frames maintain semantic consistency with the original frames while preserving the high quality generation capabilities of the diffusion model.
Solution Approach 2:
The patent applies different quality requirements to different parts of the generation process. The latent encoding focuses on capturing semantic information, while the diffusion model focuses on generating high-quality visual details. This local specialization maintains both semantic consistency and video quality.
Data Source
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 and an end frame for the video, a first caption of the start frame, and a second caption of the end frame. The method further comprises generating a first latent noise in a latent space based on the start frame and a second latent noise in the latent space based on the end frame. The method further comprises generating third latent noises in the latent space corresponding to transition frames 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, the transition frames based on the third latent noises, the start frame, the end frame, the first caption, and the second caption.