Free$^2$Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models
Jaemin Kim, Bryan S Kim, Jong Chul Ye
2024-11-29
Summary
This paper introduces Free^2Guide, a new method for improving how videos are generated from text descriptions, making it easier to align the video content with what the text says without needing complicated training.
What's the problem?
Generating videos from text can be challenging because it’s hard to ensure that the video matches the text accurately, especially since videos have many frames that need to work together. Current methods often rely on reinforcement learning, which can be limited and require specific types of feedback that are not always available.
What's the solution?
The authors propose Free^2Guide, which uses a unique approach called gradient-free path integral control. This method allows the system to improve video generation by using non-differentiable reward functions, meaning it doesn’t need traditional training methods. It can also combine different reward models to enhance video quality without needing a lot of extra computing power. This makes it more flexible and efficient in generating videos that align well with the provided text.
Why it matters?
This research is significant because it enhances the capabilities of AI in creating videos that accurately reflect text prompts. By improving how videos are generated, Free^2Guide can be applied in various fields such as entertainment, education, and marketing, ultimately leading to better and more engaging visual content.
Abstract
Diffusion models have achieved impressive results in generative tasks like text-to-image (T2I) and text-to-video (T2V) synthesis. However, achieving accurate text alignment in T2V generation remains challenging due to the complex temporal dependency across frames. Existing reinforcement learning (RL)-based approaches to enhance text alignment often require differentiable reward functions or are constrained to limited prompts, hindering their scalability and applicability. In this paper, we propose Free^2Guide, a novel gradient-free framework for aligning generated videos with text prompts without requiring additional model training. Leveraging principles from path integral control, Free^2Guide approximates guidance for diffusion models using non-differentiable reward functions, thereby enabling the integration of powerful black-box Large Vision-Language Models (LVLMs) as reward model. Additionally, our framework supports the flexible ensembling of multiple reward models, including large-scale image-based models, to synergistically enhance alignment without incurring substantial computational overhead. We demonstrate that Free^2Guide significantly improves text alignment across various dimensions and enhances the overall quality of generated videos.