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Make It Move: Controllable Image-to-Video Generation With Text Descriptions

CVPR 2022 · Video › Video generation · approach: Compositional text-to-video diffusion · 60 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use cross-attention instead of concatenation to align image and text in the motion anchor (ablation)MAGE · CATER-GEN-v1FID: 63.4 -> 62.0Helped
Use cross-attention instead of concatenation to align image and text in the motion anchor (ablation)MAGE · CATER-GEN-v1LPIPS: 0.20 -> 0.20No clear effect
Use cross-attention instead of concatenation to align image and text in the motion anchor (ablation)MAGE · CATER-GEN-v1FVD: 30.4 -> 30.5No clear effect
Use axial instead of vanilla transformers (ablation)MAGE · CATER-GEN-v1GFlops: 96.8 -> 52.7Helped
Use axial instead of vanilla transformers (ablation)MAGE · CATER-GEN-v1Throughput: 11.5 -> 37.2Helped
Use axial instead of vanilla transformers (ablation)MAGE · CATER-GEN-v1FID: 62.0 -> 62.7Hurt
Enable implicit randomness for ambiguous descriptionsMAGE · CATER-GEN-v1DIV I3D: 0 -> 0.45Helped
Enable implicit randomness for ambiguous descriptionsMAGE · CATER-GEN-v2DIV I3D: 0 -> 2.06Helped
Enable implicit randomness for ambiguous descriptionsMAGE · CATER-GEN-v1FVD: 31.70 -> 45.49Hurt
Enable implicit randomness for ambiguous descriptionsMAGE · CATER-GEN-v2FVD: 57.55 -> 69.44Hurt

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