Latent Knowledge-Guided Video Diffusion for Scientific Phenomena Generation from a Single Initial Frame
Video diffusion models have achieved impressive results in natural scene generation, yet they struggle to generalize to scientific phenomena such as fluid simulations and meteorological processes, where underlying dynamics are governed by scientific laws. To handle…
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- 1 This section discusses the limitations of current video diffusion models in generating scientifically accurate videos, particularly in fluid dynamics and meteorology. It highlights the…
- 2 This section reviews existing diffusion models for image and video generation, noting advancements in fidelity and controllability. It emphasizes the challenges faced when applying…
- 3 This section introduces latent diffusion models and quaternion neural networks, explaining their relevance to the proposed method and how they improve efficiency and quality…
- 4 This section outlines the two-stage process of the proposed framework: latent knowledge extraction and knowledge-guided generation, detailing how static and dynamic embeddings are created…
Introduction
This section discusses the limitations of current video diffusion models in generating scientifically accurate videos, particularly in fluid dynamics and meteorology. It highlights the need for models that can generalize under limited data and without natural-language prompts.
Preliminaries
This section introduces latent diffusion models and quaternion neural networks, explaining their relevance to the proposed method and how they improve efficiency and quality in video generation.
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Method Video Generation Pipeline
This section outlines the two-stage process of the proposed framework: latent knowledge extraction and knowledge-guided generation, detailing how static and dynamic embeddings are created and utilized.
Latent Knowledge Extraction
This section describes the process of extracting static and dynamic knowledge from training videos using a Masked Autoencoder and Optical Flow Predictor, emphasizing the importance of adhering to scientific laws during training.
Figures Explained
Frequently Asked Questions
Video diffusion models have achieved impressive results in natural scene generation, yet they struggle to generalize to scientific phenomena such as fluid simulations and meteorological processes, where underlying dynamics are governed by scientific laws. To handle this dilemma, we extracted the latent scientific phenomena knowledge and proposed a framework that teaches video diffusion models to generate scientific phenomena…
This section discusses the limitations of current video diffusion models in generating scientifically accurate videos, particularly in fluid dynamics and meteorology. It highlights the… This section reviews existing diffusion models for image and video generation, noting advancements in fidelity and controllability. It emphasizes the challenges faced when applying… This section introduces latent diffusion models and quaternion neural networks, explaining their relevance to the proposed method and how they improve efficiency and quality…
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