  Pose-driven human-image animation diffusion models have shown remarkable
capabilities in realistic human video synthesis. Despite the promising results
achieved by previous approaches, challenges persist in achieving temporally
consistent animation and ensuring robustness with off-the-shelf pose detectors.
In this paper, we present TCAN, a pose-driven human image animation method that
is robust to erroneous poses and consistent over time. In contrast to previous
methods, we utilize the pre-trained ControlNet without fine-tuning to leverage
its extensive pre-acquired knowledge from numerous pose-image-caption pairs. To
keep the ControlNet frozen, we adapt LoRA to the UNet layers, enabling the
network to align the latent space between the pose and appearance features.
Additionally, by introducing an additional temporal layer to the ControlNet, we
enhance robustness against outliers of the pose detector. Through the analysis
of attention maps over the temporal axis, we also designed a novel temperature
map leveraging pose information, allowing for a more static background.
Extensive experiments demonstrate that the proposed method can achieve
promising results in video synthesis tasks encompassing various poses, like
chibi. Project Page: https://eccv2024tcan.github.io/
