Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback (2412.02617v1)

Abstract: Large text-to-video models hold immense potential for a wide range of downstream applications. However, these models struggle to accurately depict dynamic object interactions, often resulting in unrealistic movements and frequent violations of real-world physics. One solution inspired by LLMs is to align generated outputs with desired outcomes using external feedback. This enables the model to refine its responses autonomously, eliminating extensive manual data collection. In this work, we investigate the use of feedback to enhance the object dynamics in text-to-video models. We aim to answer a critical question: what types of feedback, paired with which specific self-improvement algorithms, can most effectively improve text-video alignment and realistic object interactions? We begin by deriving a unified probabilistic objective for offline RL finetuning of text-to-video models. This perspective highlights how design elements in existing algorithms like KL regularization and policy projection emerge as specific choices within a unified framework. We then use derived methods to optimize a set of text-video alignment metrics (e.g., CLIP scores, optical flow), but notice that they often fail to align with human perceptions of generation quality. To address this limitation, we propose leveraging vision-LLMs to provide more nuanced feedback specifically tailored to object dynamics in videos. Our experiments demonstrate that our method can effectively optimize a wide variety of rewards, with binary AI feedback driving the most significant improvements in video quality for dynamic interactions, as confirmed by both AI and human evaluations. Notably, we observe substantial gains when using reward signals derived from AI feedback, particularly in scenarios involving complex interactions between multiple objects and realistic depictions of objects falling.

• The paper introduces a novel RL finetuning objective that integrates KL regularization and policy projection to improve dynamic object interactions.
• It leverages vision-language models for AI feedback, resulting in enhanced text-video alignment and realistic physical dynamics in multi-object scenes.
• Experimental comparisons indicate that reverse-BT projection (DPO) outperforms reward-weighted regression in aligning outputs with human perceptions.

Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback: An Expert Overview

The focal point of the paper "Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback" centers around addressing critical challenges pertaining to dynamic object interactions in text-to-video generation. Despite the growing capabilities of large text-to-video models, the generation of realistic movements and adherence to real-world physics remains problematic. This work explores a method inspired by the feedback mechanisms used in LLMs, whereby external feedback is utilized to autonomously refine model outputs.

The paper proposes a probabilistic framework for offline reinforcement learning (RL) finetuning, accompanied by a comprehensive analysis of algorithmic choices and feedback types that contribute to improved text-video alignment and more realistic object interactions. A central innovation of this paper is the derivation of a unified RL-finetuning objective, highlighting the intrinsic connections among existing algorithms such as KL regularization and policy projection within this framework.

Experimentally, the paper explores the optimization of various text-video alignment metrics, noting that many of these metrics do not align with human perceptions of quality. To mitigate this discrepancy, the authors propose leveraging vision-LLMs (VLMs) to generate more nuanced feedback, tailored specifically towards video object dynamics. Empirical results demonstrate that this approach effectively enhances video quality, particularly in scenarios involving complex multi-object interactions and faithfully depicting realistic physics, such as objects falling under gravity.

From a methodological perspective, the paper compares two fundamental RL-finetuning approaches: forward-EM-projection (mainly represented as reward-weighted regression — RWR) and reverse-BT-projection (represented as direct preference optimization — DPO). Both approaches exhibit distinct advantages and limitations. Notably, reverse-BT-projection, employed in DPO, reveals superior performance across a variety of evaluation metrics compared to forward-EM-projection, but can be prone to over-optimization issues, particularly when feedback is derived from metric-based rewards.

Significantly, preference evaluations conducted by both AI and human evaluators indicate that VLMs serve as effective proxies for human feedback. VLM-based feedback, termed AI Feedback (AIF), emerged as the most effective in aligning outputs with desired qualities across training and testing scenarios, surpassing traditional metric-based feedback methods such as CLIP scores and optical flow.

The implications of this research are manifold, suggesting a transformative pathway for refining video generation models to handle the complexity of dynamic scenes. It anticipates an increasing role for VLMs in automating feedback for both model training and evaluation processes, offering a scalable and cost-effective alternative to conventional human evaluators. This approach is particularly pertinent in applications demanding high fidelity and realism in generated content, such as virtual reality, animation, and robotic simulation environments.

From a theoretical vantage, the paper reinforces the notion that models benefiting from fine-grained feedback mechanisms can perform nuanced adjustments mirroring human evaluators' assessments. Moving forward, further exploration into the integration of more advanced VLMs and the refinement of feedback correlation mechanisms could propel advancements in the alignment of model outputs with human-like understanding and expectations. As models continue to evolve, embedding these insights could eventually revolutionize text-to-video generation tasks, mitigating existing limitations and unlocking new dimensions of application.