Motion Forcing: A Decoupled Framework for Robust Video Generation in Motion Dynamics

arXiv:2603.10408 · cs.CV · Submitted 2026-03-11 · Read on arXiv

cs.CV

Submitted: 2026-03-11

Updated: 2026-09-26

Code: https://github.com/Tianshuo-Xu/Motion-Forcing

Terminology

• Diffusion Model — 14× in this paper · explained in 8 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• World Model — 8× in this paper · explained in 7 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Optical Flow — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Entanglement — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Physical Constraints — 3× in this paper · explained in 7 episodes
• Agent Interactions — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embodied AI — 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
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• VideoPhy: Evaluating Physical Commonsense for Video Generation
• Physion: Evaluating Physical Prediction from Vision in Humans and Machines
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
• VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
• STANCE: Motion Coherent Video Generation Via Sparse-to-Dense Anchored Encoding
• Video Prediction Models as Rewards for Reinforcement Learning
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
• CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
• GAIA-1: A Generative World Model for Autonomous Driving
• Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
• How Far is Video Generation from World Model: A Physical Law Perspective
• Learning to Act from Actionless Videos through Dense Correspondences
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• PhysVideoGenerator: Towards Physically Aware Video Generation via Latent Physics Guidance
• DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model
• Flow Matching for Generative Modeling
• Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
• Fr'echet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
• 4D Driving Scene Generation With Stereo Forcing

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