With the impressive progress in diffusion-based text-to-image generation,
extending such powerful generative ability to text-to-video raises enormous
attention. Existing methods either require large-scale text-video pairs and a
large number of training resources or learn motions that are precisely aligned
with template videos. It is non-trivial to balance a trade-off between the
degree of generation freedom and the resource costs for video generation. In
our study, we present a few-shot-based tuning framework, LAMP, which enables
text-to-image diffusion model Learn A specific Motion Pattern with 8~16 videos
on a single GPU. Specifically, we design a first-frame-conditioned pipeline
that uses an off-the-shelf text-to-image model for content generation so that
our tuned video diffusion model mainly focuses on motion learning. The
well-developed text-to-image techniques can provide visually pleasing and
diverse content as generation conditions, which highly improves video quality
and generation freedom. To capture the features of temporal dimension, we
expand the pretrained 2D convolution layers of the T2I model to our novel
temporal-spatial motion learning layers and modify the attention blocks to the
temporal level. Additionally, we develop an effective inference trick,
shared-noise sampling, which can improve the stability of videos with
computational costs. Our method can also be flexibly applied to other tasks,
e.g. real-world image animation and video editing. Extensive experiments
demonstrate that LAMP can effectively learn the motion pattern on limited data
and generate high-quality videos. The code and models are available at
this https URL

通过少样本调参框架 LAMP，本研究展示了如何在单个 GPU 上学习特定的运动模式，以便用较少的数据生成高质量视频。