AICL: Action In-Context Learning for Text-to-Video Generation

摘要

Recent large-scale video datasets have facilitated the generation of diverse videos of Video Diffusion Models (VDMs). Nonetheless, some complex actions have still struggled to be generated by those VDMs, leading to a reduction in video generalization. Some researchers attempt to use video editing methods for complex action generation. However, the actions in generated videos are often identical to the reference video, resulting in a lack of diversity. To this end, we first propose Acton In-Context Learning (AICL), a novel approach to generate intricate actions by emulating motions from pre-existing videos in the inference stage using a plug-and-play method. Specifically, the Action Perceiver (AP), is introduced to distill action features from reference videos, which requires training on only a small dataset. Leveraging the knowledge from pre-trained VDMs, Action Integration is introduced for incorporating new action features extracted by AP into VDMs through the additional layers. Extensive experiments demonstrate that AICL is not merely replicating the motion from references, and it significantly improves the generation of realistic actions, even in situations where existing VDMs might directly fail.