MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation

TL;DR

This paper introduces MJ-BENCH-VIDEO, a comprehensive benchmark for evaluating video generation quality across multiple aspects, and proposes MJ-VIDEO, a new reward model that improves preference judgment accuracy and enhances video alignment.

Contribution

The paper presents a large-scale, fine-grained video preference benchmark and a novel MoE-based reward model that outperforms existing methods in preference assessment and tuning.

Findings

MJ-VIDEO achieves 17.58% improvement in overall preference judgment.

The benchmark covers five critical aspects with 28 criteria.

MJ-VIDEO enhances video alignment in generation tasks.

Abstract

Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions. However, existing models still struggle with key challenges such as instruction misalignment, content hallucination, safety concerns, and bias. Addressing these limitations, we introduce MJ-BENCH-VIDEO, a large-scale video preference benchmark designed to evaluate video generation across five critical aspects: Alignment, Safety, Fineness, Coherence & Consistency, and Bias & Fairness. This benchmark incorporates 28 fine-grained criteria to provide a comprehensive evaluation of video preference. Building upon this dataset, we propose MJ-VIDEO, a Mixture-of-Experts (MoE)-based video reward model designed to deliver fine-grained reward. MJ-VIDEO can dynamically select relevant experts to accurately judge the preference based on the input text-video pair. This architecture enables more precise and adaptable preference judgments. Through extensive benchmarking on MJ-BENCH-VIDEO, we analyze the limitations of existing video reward models and demonstrate the superior performance of MJ-VIDEO in video preference assessment, achieving 17.58% and 15.87% improvements in overall and fine-grained preference judgments, respectively. Additionally, introducing MJ-VIDEO for preference tuning in video generation enhances the alignment performance. All our code, data, and models are available at https://aiming-lab.github.io/MJ-VIDEO.github.io/.