  We introduce a method to generate temporally coherent human animation from a
single image, a video, or a random noise. This problem has been formulated as
modeling of an auto-regressive generation, i.e., to regress past frames to
decode future frames. However, such unidirectional generation is highly prone
to motion drifting over time, generating unrealistic human animation with
significant artifacts such as appearance distortion. We claim that
bidirectional temporal modeling enforces temporal coherence on a generative
network by largely suppressing the motion ambiguity of human appearance. To
prove our claim, we design a novel human animation framework using a denoising
diffusion model: a neural network learns to generate the image of a person by
denoising temporal Gaussian noises whose intermediate results are
cross-conditioned bidirectionally between consecutive frames. In the
experiments, our method demonstrates strong performance compared to existing
unidirectional approaches with realistic temporal coherence.
