Diffusion Video Generation Using First-Last Frame Guidance
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Solution Overview
Problem
Existing video generation methods using machine learning models often produce videos with poor dynamicity and lack realistic motion effects, struggling to create high-dynamic action videos and complex camera movements.
Innovation Solution
A machine learning architecture based on a diffusion model that combines image instructions of a first and last frame with text instructions for video generation, using a pre-trained text encoder and variational autoencoder to encode text and image information, and a diffusion model to generate coherent and diverse videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a machine learning model is used to generate videos from images and text, then video generation automation is improved, but the dynamicity and realism of motion effects deteriorate
Solution Approach 1:
The patent segments the video generation process into multiple independent modules: image encoding module, text encoding module, diffusion model module, and decoding module. Each module processes specific aspects (spatial features, temporal features, motion dynamics) separately before integration, allowing optimized processing of motion realism while maintaining automation.
Solution Approach 2:
The patent introduces intermediate representations including latent space embeddings, motion vectors, and feature maps as mediators between input images/text and final video output. These intermediaries enable sophisticated motion transformation and temporal coherence without requiring direct pixel-to-pixel mapping, thereby improving motion realism while maintaining automation.
2Extent of automation
If existing video generation methods are used, then automation is achieved, but the complexity of generating high-dynamic action videos increases
Solution Approach 1:
The patent implements dynamic adaptive processing where the diffusion model adjusts its transformation parameters based on input characteristics. Motion vectors and temporal features are dynamically computed rather than statically predefined, enabling the system to handle high-dynamic action videos automatically without requiring complex manual configuration.
Solution Approach 2:
The patent utilizes parameter changes in the diffusion process including variable noise schedules, adaptive sampling rates, and dynamic threshold adjustments during video generation. These parameter modifications enable the automated system to efficiently generate diverse video content including high-dynamic scenes without increasing structural complexity.
3Measurement precision
If manual data labeling is used for training, then model accuracy is improved, but the time and cost for data preparation increases
Solution Approach 1:
The patent implements self-service mechanisms where the model performs self-supervised learning using inherent structures in the training data. The diffusion model learns from unlabelled video-data pairs by exploiting temporal consistency and motion patterns automatically, eliminating the need for manual annotation while maintaining high accuracy through iterative refinement and feedback loops.
Data Source
AI summary
Provided area method and an apparatus for generating a video, a device, and a medium. In one method, a first reference image and a second reference image are determined from a plurality of reference images in a reference video. A reference text for describing the reference video is received. A generation model is acquired based on the first reference image, the second reference image and the reference text. The generation model is configured to generate a target video based on a first image, a second image and a text. With example implementations of the present disclosure, the second reference image can serve as guiding data to determine a development direction of a story in the video. In this way, the generation model can clearly grasp changes of various image contents in the video, which is beneficial to generating richer and more realistic videos.