MotionInsight: Diagnosing Object Motion Deficiencies in Generated Videos

arXiv:2609.37030 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/JohnZhan2023/MotionInsight

Terminology

• Consistency — 35× in this paper · explained in 3 episodes
• Fidelity — 21× in this paper · explained in 2 episodes
• Alignment — 17× in this paper · explained in 1 episode
• GRPO — 15× in this paper · explained in 6 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Semantic Alignment — 6× in this paper · explained in 3 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 4 episodes
• Evaluation Dimensions — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mean Opinion Score — 1× in this paper
• Optical Flow — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• World Simulation with Video Foundation Models for Physical AI
• Qwen3-VL Technical Report
• VideoPhy: Evaluating Physical Commonsense for Video Generation
• VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
• The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation
• SAM 3: Segment Anything with Concepts
• PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
• HumanScore: Benchmarking Human Motions in Generated Videos
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• LTX-Video: Realtime Video Latent Diffusion
• VideoScore2: Think before You Score in Generative Video Evaluation
• Benchmarking Scientific Understanding and Reasoning for Video Generation using VideoScience-Bench
• ViPE: Video Pose Engine for 3D Geometric Perception
• GPT-4o System Card
• The Kinetics Human Action Video Dataset
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• WorldModelBench: Judging Video Generation Models As World Models
• Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation
• TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility
• OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation

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