Brief context
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Original paper
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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.
Executive brief
A short business-reader brief that explains why the paper matters now and what to watch or do next.
Why this is worth your attention
Text-to-video models are getting good at making plausible-looking clips, but this paper shows a harder commercial truth: they still often fail at the part many real workflows actually need—showing an object physically change in the right way over time. That matters for product teams, creative tooling buyers, and anyone betting on AI video for demos, training, commerce, or simulation, because “looks right” is not the same as “did the right thing.” The evidence here is strong enough to challenge vendor claims on controllability, but it is still a benchmark paper in a cooking-heavy domain, not proof that all video generation use cases are blocked.
- If your use case depends on a product visibly changing state—cut, peeled, squeezed, mashed, assembled—today’s T2V systems may be much less reliable than demo reels suggest. The paper shows a consistent gap between high semantic alignment and lower object-state-change accuracy and consistency, which is exactly the failure mode that breaks training, instructional, commerce, and simulation content.
- Ask vendors for prompt-level evidence on state transitions, not just text alignment or visual quality scores. This paper suggests a model can recognize the right objects and scene yet still fail the actual transformation, especially in novel or multi-step prompts.
- Revisit the assumption that better-looking video means better controllability. Proprietary models lead here, but even the best reported system still shows a noticeable drop from subject/object alignment to OSC accuracy and consistency, so quality gains alone do not solve action fidelity.
- A meaningful next signal is whether vendors start publishing state-change benchmarks or product features for multi-step action control, rather than only cinematic quality metrics. The paper also suggests MLLM-based evaluation can help scale this testing, but not yet replace humans for fine-grained quality assurance.
- This is a useful diagnostic benchmark, not a full market readout. It is centered on cooking-related manipulations, the human study samples one prompt per scenario rather than every prompt, and the automated judges correlate with humans only moderately well in some areas, so treat the conclusion as directionally important rather than universally settled.
Evidence ledger
Current T2V models remain materially weaker at object state change than at subject/object or scene alignment.
Novel and compositional prompts expose brittle generalization and tendency toward memorized patterns.
MLLM-based evaluation is more useful than simple similarity scoring for OSC benchmarking, but not reliable enough to treat as a full human substitute.
OSCBench is a substantial, purpose-built benchmark for diagnosing state-aware video generation.
The benchmark’s domain concentration limits how broadly the results should be generalized.
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