VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation (2412.21059v2)

Abstract: Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimensions remains a persistent challenge. Though reinforcement learning from human feedback offers promise for preference alignment, existing reward models for visual generation face limitations, including black-box scoring without interpretability and potentially resultant unexpected biases. We present VisionReward, a general framework for learning human visual preferences in both image and video generation. Specifically, we employ a hierarchical visual assessment framework to capture fine-grained human preferences, and leverages linear weighting to enable interpretable preference learning. Furthermore, we propose a multi-dimensional consistent strategy when using VisionReward as a reward model during preference optimization for visual generation. Experiments show that VisionReward can significantly outperform existing image and video reward models on both machine metrics and human evaluation. Notably, VisionReward surpasses VideoScore by 17.2% in preference prediction accuracy, and text-to-video models with VisionReward achieve a 31.6% higher pairwise win rate compared to the same models using VideoScore. All code and datasets are provided at https://github.com/THUDM/VisionReward.

• The paper introduces a reward model that dissects fine-grained human preferences to guide image and video generation.
• It proposes a Multi-Objective Preference Optimization (MPO) algorithm to balance diverse aesthetic and dynamic factors.
• Empirical results show a 17.2% accuracy boost over baseline models, demonstrating enhanced generative alignment with human standards.

An Analysis of "VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation"

The paper "VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation" introduces a novel framework designed to enhance the alignment of visual generative models with human preferences. Specifically, the authors propose and investigate VisionReward, a comprehensive reward model tailored for both image and video generation. This work is distinguished by its incorporation of fine-grained, multi-dimensional learning to capture nuanced human preferences and apply them as an optimization strategy for generative models.

Detailed Breakdown of the VisionReward Framework

VisionReward consists of a reward model that meticulously dissects human preferences across multiple dimensions, each corresponding to a distinct set of evaluative questions. These are linearly weighted to compute an aggregate preference score. The core innovation lies in its approach to challenge traditional assessment techniques by structuring dimensions that span fidelity, aesthetic quality, and dynamic features, especially pertinent in video evaluation.

The empirical results presented indicate a marked improvement over existing models like VideoScore, with VisionReward achieving a 17.2% increase in predictive accuracy for video preferences. This substantial gain underscores the effectiveness of integrating a comprehensive, dynamic-feature-focused assessment strategy.

Multi-Objective Preference Learning via MPO

A significant contribution of the paper is the development of a Multi-Objective Preference Optimization (MPO) algorithm. This is particularly crucial for addressing confounding variables present in human preference data, ensuring that learning is not biased towards certain dimensions at the expense of others. The authors provide a nuanced analysis of how MPO enables diffusion models to be tuned more effectively across various factors without over-optimization, a common pitfall in reinforcement learning frameworks applied to generative models.

Experimental Validation

The paper presents rigorous experimental validation involving a multi-faceted test set for assessing the alignment of generated outputs with human preferences. The results demonstrate that VisionReward significantly outperforms baseline methods across multiple datasets, corroborating the robustness and scalability of its multi-dimensional approach. Notably, results from VisionReward are said to offer improved interpretability, bolstering its utility as a tool for assessing and optimizing generative models in complex visual domains.

Implications and Prospective Applications

This paper opens avenues for further research in refining the trajectories of visual generative models to fine-tune outputs based on complex human preferences. By advancing a more granular understanding of these preferences and providing a framework for their application, VisionReward offers significant potential for enhancing interactive AI systems, where user engagement is contingent upon the visual appeal and authenticity of the generated content.

Furthermore, VisionReward and the MPO strategy have meaningful implications for broader AI developments. As AI systems increasingly permeate creative domains, there's a growing demand for nuanced, human-aligned content generation. By improving the calibration process towards human-centric metrics, tools like VisionReward can drive significant advancements in the deployment of AI across both commercial and artistic sectors.

Conclusion

In conclusion, "VisionReward" is a commendable step toward bridging the gap between artificial content generation and human aesthetic standards. It provides a methodological and empirical foundation for future innovations in aligning digital content with human desires, paving the way for richer, more interactive, and aesthetically pleasing AI-generated experiences. Continued exploration of multi-dimensional preference frameworks will undoubtedly enrich the field of AI with nuanced insights and adaptations that mirror the complexities inherent in human preferences.