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PVBench: Quantifying Physical Plausibility in Video Generation with Object-Level Physical Measurements

Kaiwei Chen, Moran Li, 甘振业, Yabiao Wang, Chengjie Wang, Kexin Zhang, Yong Liu, Jiangning Zhang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26711 · cited by 0 works (OpenAlex) · open-access PDF

Korean

최신 리소스 비디오 생성 모델을 통해 유효한 오픈 프로토콜은 시각적으로 매력적인 출력을 생산하지만, 그들의 물리적 합리성은 비디오 생성 자체보다 약간 측정됩니다. 물리적 합리성은 생성 된 객체가 지오메트리, 키네미틱, 역학 및 보존법에 따라 평가하는지 여부를 묻습니다. 기존 벤치마크는 시각적 언어 모델의 거친 판단이나 인체화 된 세계 모델을 목표로합니다. 우리는 PVBench를 소개합니다. 물리적 비디오 벤치마크는 46 차원에 걸쳐 생성 된 비디오의 합리성을 정량적으로 측정하는 반면, vi) kinematic and spatiotoral persistence, vii) 및 plausifiability의 메커니즘을 7 개 영

Machine translation (Meta M2M100); the English original follows.

Abstract

Recent video generation models produce visually compelling outputs, yet their physical plausibility remains poorly measured. Physical plausibility asks whether generated objects obey geometry, kinematics, dynamics, and conservation laws. Existing benchmarks rely on coarse visual-language model judgments or target embodied world models rather than video generation itself. We introduce PVBench, a physical video benchmark that quantitatively measures the plausibility of generated videos across 46 dimensions organized into seven domains, spanning: i) instruction and semantic fidelity, ii) behavioral and causal consistency, iii) perceptual and temporal quality, iv) geometric and spatial consistency, v) material properties and field states, vi) kinematic and spatiotemporal persistence, vii) and mechanical plausibility with conservation laws. PVBench comprises 600 carefully crafted prompts, of which 30% involve physical embodied agents such as robot arms, humanoids, quadrupeds, and multi-agent systems. For challenging physically tractable dimensions, it pairs each with specialized vision models that derive verifiable, physically grounded measurements, such as object-level 6D pose, metric scale, surface normals, kinematics, and numerical conservation-law residuals. It thus avoids relying solely on subjective scoring, while supporting both text-to-video and image-to-video generation. Finally, we validate the agreement between automatic metrics and human judgments through human evaluation. Meanwhile, we evaluate mainstream open-source and closed-source models and present several insights. By releasing the benchmark, prompts, and calibration protocol, we aim to foster physically grounded video generation.

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Topics: Computer science, Embodied cognition, Metric (unit), Artificial intelligence, Benchmark (surveying), Field (mathematics)

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