A new investigation into video diffusion models finds that their tendency to generate content violating real-world physical laws stems from flaws in the models' internal attention mechanisms rather than from missing external priors or specialized training data. The research examines the internal mechanisms of state-of-the-art video diffusion models to identify the root cause, noting that existing solutions have relied on external priors or specialized data despite the models' impressive visual quality.
Despite impressive visual quality, state-of-the-art video diffusion models often generate content that violates real-world physical laws. While existing solutions rely on external priors or specialized data, we investigate the root cause by exploring the internal mechanisms of these models. Specific
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