PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos

arXiv:2609.38377 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Consistency — 66× in this paper · explained in 3 episodes
• Benchmark — 63× in this paper · explained in 2 episodes
• Accuracy — 30× in this paper · explained in 1 episode
• Calibration — 15× in this paper · explained in 7 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Aggregation — 8× in this paper · explained in 1 episode
• Visual Quality — 7× in this paper
• Ablation — 6× in this paper · explained in 1 episode
• Core — 6× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Distribution Shift — 4× in this paper · explained in 5 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Anchor Loss — 3× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Exponential Moving Average — 1× in this paper
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Motion Quality — 1× in this paper
• Multi-Layer Perceptron — 1× in this paper
• Optical Flow — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Impossible Videos
• VideoPhy: Evaluating Physical Commonsense for Video Generation
• VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
• Physion: Evaluating Physical Prediction from Vision in Humans and Machines
• Perception Encoder: The best visual embeddings are not at the output of the network
• IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
• A Short Note on the Kinetics-700 Human Action Dataset
• A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame Prediction
• DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning
• LTX-2: Efficient Joint Audio-Visual Foundation Model
• VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
• VideoScore2: Think before You Score in Generative Video Evaluation
• GenAI Arena: An Open Evaluation Platform for Generative Models
• The Kinetics Human Action Video Dataset
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
• WorldModelBench: Judging Video Generation Models As World Models
• BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos
• Improving Video Generation with Human Feedback
• Decoupled Weight Decay Regularization

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