Liu Hanchen commited on
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Update README.md

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  <img src="overview/comparison.png" width="720" alt="Comparison">
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- (a) In current I2V-DM-based methods, the end-frame constraint is weaker than the start-frame constraint due to the same injection mechanism but a smaller training scale, causing a distorted predicted path with collapsed content. \\
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  (b) Our Sci-Fi maintains start frame processing while enhancing end-frame constraint injection. This achieves symmetric start-end-frame constraints with small training, yielding a fine predicted path close to the real one with smoother inbetweening.
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  ## Some challenging examples of our Sci-Fi for frame inbetweening.
 
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  <div align="center">
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  <img src="overview/comparison.png" width="720" alt="Comparison">
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+ (a) In current I2V-DM-based methods, the end-frame constraint is weaker than the start-frame constraint due to the same injection mechanism but a smaller training scale, causing a distorted predicted path with collapsed content.<\br><\br>
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  (b) Our Sci-Fi maintains start frame processing while enhancing end-frame constraint injection. This achieves symmetric start-end-frame constraints with small training, yielding a fine predicted path close to the real one with smoother inbetweening.
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  ## Some challenging examples of our Sci-Fi for frame inbetweening.