OSCBench: Benchmarking Object State Change in Text-to-Video Generation

TL;DR

This paper introduces OSCBench, a benchmark for evaluating object state change understanding in text-to-video models, revealing current models' struggles with accurate, consistent object transformations.

Contribution

The paper presents OSCBench, a novel benchmark for assessing object state change in T2V models, highlighting a critical gap in current evaluation methods.

Findings

Current T2V models excel at scene and semantic alignment.

Models struggle with accurate, temporally consistent object state changes.

Performance drops in novel and compositional scenarios.

Abstract

Text-to-video (T2V) generation models have made rapid progress in producing visually high-quality and temporally coherent videos. However, existing benchmarks primarily focus on perceptual quality, text-video alignment, or physical plausibility, leaving a critical aspect of action understanding largely unexplored: object state change (OSC) explicitly specified in the text prompt. OSC refers to the transformation of an object's state induced by an action, such as peeling a potato or slicing a lemon. In this paper, we introduce OSCBench, a benchmark specifically designed to assess OSC performance in T2V models. OSCBench is constructed from instructional cooking data and systematically organizes action-object interactions into regular, novel, and compositional scenarios to probe both in-distribution performance and generalization. We evaluate six representative open-source and proprietary T2V models using both human user study and multimodal large language model (MLLM)-based automatic evaluation. Our results show that, despite strong performance on semantic and scene alignment, current T2V models consistently struggle with accurate and temporally consistent object state changes, especially in novel and compositional settings. These findings position OSC as a key bottleneck in text-to-video generation and establish OSCBench as a diagnostic benchmark for advancing state-aware video generation models.