Meet LAMP: A Few-Shot AI Framework for Learning Motion Patterns with Text-to-Image Diffusion Models

In a recent study, researchers have introduced a groundbreaking few-shot-based tuning framework called LAMP, designed to address the challenge of text-to-video (T2V) generation. While text-to-image (T2I) generation has made significant progress, extending this capability to text-to-video has been a complex problem. Existing methods either require extensive text-video pairs and significant computational resources or result in video generation that is heavily aligned with template videos. Balancing generation freedom and resource costs for video generation has proven to be a challenging trade-off.

A team of researchers from VCIP, CS, Nankai University, and MEGVII Technology propose LAMP as a solution to this problem. LAMP is a few-shot-based tuning framework that allows a text-to-image diffusion model to learn specific motion patterns with only 8 to 16 videos on a single GPU. This framework employs a first-frame-conditioned pipeline that uses a pre-trained text-to-image model for content generation, focusing the video diffusion model’s efforts on learning motion patterns. By using well-established text-to-image techniques for content generation, LAMP significantly improves video quality and generation freedom.

To capture the temporal features of videos, the researchers extend the 2D convolution layers of the pre-trained T2I model to incorporate temporal-spatial motion learning layers. They also modify attention blocks to work at the temporal level. Additionally, they introduce a shared-noise sampling strategy during inference, which enhances video stability with minimal computational costs.

LAMP’s capabilities extend beyond text-to-video generation. It can also be applied to tasks like real-world image animation and video editing, making it a versatile tool for various applications.

Extensive experiments were conducted to evaluate LAMP’s performance in learning motion patterns on limited data and generating high-quality videos. The results show that LAMP can effectively achieve these goals. It successfully strikes a balance between training burden and generation freedom while understanding motion patterns. By leveraging the strengths of T2I models, LAMP offers a powerful solution for text-to-video generation.

In conclusion, the researchers have introduced LAMP, a few-shot-based tuning framework for text-to-video generation. This innovative approach addresses the challenge of generating videos from text prompts by learning motion patterns from a small video dataset. LAMP’s first-frame-conditioned pipeline, temporal-spatial motion learning layers, and shared-noise sampling strategy significantly improve video quality and stability. The framework’s versatility allows it to be applied to other tasks beyond text-to-video generation. Through extensive experiments, LAMP has demonstrated its effectiveness in learning motion patterns on limited data and generating high-quality videos, offering a promising solution to the field of text-to-video generation.

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