Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms
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. Specifically, we present the first interpretability study on the ''motion planning'' pro
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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. Specifically, we present the first interpretability study on the ''motion planning'' pro
Understanding the internal mechanisms of video diffusion models, especially regarding motion planning, is crucial for developing more physically accurate and robust generative AI. This research provides insights into how attention heads contribute to motion, which can lead to more controllable and higher-quality video generation.