MiMaAlign: Hierarchical Micro-Macro Alignment for Physically Plausible Video Generation
cs.CV
Submitted: 2026-10-12
Updated: 2026-10-12
Comments: 20 pages, 14 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- World Simulation with Video Foundation Models for Physical AI
- Qwen2.5-VL Technical Report
- VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
- Physion: Evaluating Physical Prediction from Vision in Humans and Machines
- MoAlign: Motion-Centric Representation Alignment for Video Diffusion Models
- Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
- Seedance 1.0: Exploring the Boundaries of Video Generation Models
- How Far is Video Generation from World Model: A Physical Law Perspective
- HunyuanVideo: A Systematic Framework For Large Video Generative Models
- Open-Sora Plan: Open-Source Large Video Generation Model
- Phy124: Fast Physics-Driven 4D Content Generation from a Single Image
- Competition, Persuasion, and Search
- Reasoning Physical Video Generation with Diffusion Timestep Tokens via Reinforcement Learning
- OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation
- DINOv2: Learning Robust Visual Features without Supervision
- Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
- Video Event Reasoning and Prediction by Fusing World Knowledge from LLMs with Vision Foundation Models
- LongCat-Video Technical Report
- U-REPA: Aligning Diffusion U-Nets to ViTs
- Wan: Open and Advanced Large-Scale Video Generative Models
Related papers
- Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
- AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
- Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
- TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
- A Survey on Efficient Vision-Language-Action Models