CompAdapt: Adaptable Composite Motion Modeling for Physics-Consistent Text-to-Video Generation

arXiv:2609.21455 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-24

Terminology

• Adaptation — 44× in this paper · explained in 1 episode
• Consistency — 22× in this paper · explained in 3 episodes
• Benchmark — 18× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Core — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Motion Type Composite — 4× in this paper
• Physical Invariance Score — 4× in this paper
• Neural Newtonian Dynamics — 3× in this paper
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Regularization — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Additivity — 1× in this paper · explained in 1 episode
• Adversarial scenarios — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Conservation Laws — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Momentum transfer — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Ordinary Differential Equations (ODEs) — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
• VGGRPO: Towards World-Consistent Video Generation with 4D Latent Reward
• Physion: Evaluating Physical Prediction from Vision in Humans and Machines
• VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
• A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame Prediction
• Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance
• Lagrangian Neural Networks
• Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals
• Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals
• T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
• CameraCtrl: Enabling Camera Control for Text-to-Video Generation
• Mojito: Motion Trajectory and Intensity Control for Video Generation
• Imagen Video: High Definition Video Generation with Diffusion Models
• VBench: Comprehensive Benchmark Suite for Video Generative Models
• VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
• Epipolar Geometry Improves Video Generation Models
• MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance
• I-PHYRE: Interactive Physical Reasoning
• MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent

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