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Learning Dynamic Generator Model by Alternating Back-Propagation through Time

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Figure

Figure 1: Generating dynamic textures. For each category,the first row displays 6 frames of the observed sequence, andthe second and third rows show the corresponding frames oftwo synthesized sequences generated by the learned model.
Figure 4: Video interpolation by interpolating between ap-pearance latent vectors of videos at the two ends
Table 2: Recovery errors in occlusion experiments
Figure 6: Learn to remove content for background inpaint-ing. For each experiment, the first row displays 5 imageframes of the original video
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