EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation

2026-05-22

AI Summary

Current evaluation methods for video generation are largely confined to basic prompt adherence—assessing whether outputs are “correct”—while neglecting critical dimensions of cinematic excellence such as aesthetic quality, performative expressiveness, and audiovisual coherence that determine whether results are truly “excellent.” Moreover, existing automated metrics lack professional credibility. To bridge this gap, this work proposes EvalVerse, the first framework to formalize expert knowledge from professional film production into a structured evaluation taxonomy. Leveraging large-scale expert-annotated data, EvalVerse employs expert-calibrated fine-tuning of vision-language models combined with chain-of-thought reasoning to enable a qualitative leap from correctness to excellence. The framework supports fine-grained diagnostics for multi-shot sequences and complex audiovisual tasks, significantly enhancing assessment fidelity for professional-grade video quality while remaining compatible with conventional metrics, thereby establishing a reliable infrastructure for reward modeling and intelligent evaluation.

Abstract

The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community transitions towards Reinforcement Learning (RL) and agentic workflows. However, reliable evaluation has emerged as a critical bottleneck. Existing benchmarks predominantly evaluate ''whether it is right'' (basic prompt-following) while fundamentally neglecting ''whether it is good'' (cinematic quality, acting, and aesthetics). Furthermore, current automated metrics lack the domain-specific rigor required to provide trustworthy signals, creating a severe credibility gap between human aesthetic perception and machine scoring. To bridge this gap, we introduce EvalVerse, a comprehensive, pipeline-aware, and expert-calibrated evaluation framework. We treat video generation assessment not merely as an engineering task, but as a core scientific problem: the systematic digitization of subjective cinematic expertise. First, we organize domain knowledge into an evaluation taxonomy aligned with the professional filmmaking workflow (pre-production, production, and post-production). Second, we distill human expert judgments into a curated dataset with large-scale human annotations. Third, we inject this knowledge into Vision-Language Models (VLMs) through an expert-calibrated fine-tuning strategy, enabling the VLM to perform explicit Chain-of-Thought reasoning. Compared to previous works, EvalVerse not only retains compatibility with foundational ''rightness'' metrics, but also significantly expands the criteria to ''goodness'' and broaden the task coverage to complex multi-shot sequencing and audio-visual integration. Consequently, by providing granular diagnostic signals, EvalVerse transcends a static leaderboard and establishes a fundamental infrastructure for future work, such as reward models and evaluator agent.

Problem

• cinematic video generation
• evaluation benchmark
• aesthetic quality
• expert calibration
• automated metrics

Innovation

• pipeline-aware evaluation
• expert-calibrated VLM
• cinematic video generation
• Chain-of-Thought reasoning
• aesthetic benchmarking