HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks

TL;DR

HumanVBench is a new benchmark with automated pipelines for evaluating nuanced human-centric video understanding in MLLMs, revealing significant gaps in current models' capabilities.

Contribution

We introduce a scalable, automated benchmark construction method and provide the first comprehensive evaluation of MLLMs on nuanced human-centric video tasks.

Findings

Current MLLMs struggle with subtle emotions and speech-visual alignment.

Even top models underperform compared to humans on HumanVBench.

Open-sourced benchmark and pipelines to foster future research.

Abstract

Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, as existing benchmarks often overlook the intricacies of emotion, behavior, and cross-modal alignment. We introduce HumanVBench, a comprehensive video benchmark designed to rigorously probe these capabilities across 16 fine-grained tasks. A cornerstone of our work is a novel and scalable benchmark construction methodology, featuring two automated pipelines that synthesize high-quality video annotations and challenging multiple-choice questions with minimal human labor. By leveraging state-of-the-art models for annotation and systematically converting model-induced errors into plausible distractors, our framework provides a generalizable ``machine'' for creating nuanced evaluation suites. Our extensive evaluation of 30 leading MLLMs on HumanVBench reveals critical deficiencies, particularly in perceiving subtle emotions and aligning speech with visual cues, with even top proprietary models falling short of human performance. We open-source HumanVBench and our synthesis pipelines to catalyze the development of more socially intelligent and capable video MLLMs.