# MotionScape: A Motion-Stratified UAV Video Benchmark for World Modeling and Future Video Generation

arXiv: https://arxiv.org/abs/2604.07991 · HTML: https://mlsanity.com/paper/2604.07991/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [CameraCtrl: Enabling Camera Control for Text-to-Video Generation](https://mlsanity.com/paper/2404.02101/) (2024)
- [MAGI-1: Autoregressive Video Generation at Scale](https://mlsanity.com/paper/2505.13211/) (2025)
- [Learning Blind Video Temporal Consistency](https://mlsanity.com/paper/1808.00449/) (2018)
- [How Far is Video Generation from World Model: A Physical Law Perspective](https://mlsanity.com/paper/2411.02385/) (2024)
- [World Simulation with Video Foundation Models for Physical AI](https://mlsanity.com/paper/2511.00062/) (2025)
- [Training Agents Inside of Scalable World Models](https://mlsanity.com/paper/2509.24527/) (2025)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models](https://mlsanity.com/paper/2503.10592/) (2025)
- [Sekai: A Video Dataset towards World Exploration](https://mlsanity.com/paper/2506.15675/) (2025)
- [Deep Drone Racing: Learning Agile Flight in Dynamic Environments](https://mlsanity.com/paper/1806.08548/) (2018)
- [Optical Flow in Mostly Rigid Scenes](https://mlsanity.com/paper/1705.01352/) (2017)
- [AirScape: An Aerial Generative World Model with Motion Controllability](https://mlsanity.com/paper/2507.08885/) (2025)

## Built on by (most cited first, 1 of 1)

- [UAVs Meet Embodied Intelligence: Bridging Human Intents and Flying Dynamics Via Harnessing Physical-Digital AI Agents](https://mlsanity.com/paper/2609.18326/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are increasingly crucial for low-altitude autonomy and complex environment understanding. World models enable UAVs to anticipate how future states may evolve under potential actions, providing predictive support for autonomous decision-making. For video world models, future video generation serves as a means of simulating future visual states, providing a direct basis for evaluating their predictive capability. However, existing UAV video resources typically focus on specific control signals or simulated environments. Standardized benchmarks for multi-condition UAV-view future video generation are still limited. To bridge this gap, we introduce MotionScape, a real-world UAV-view benchmark comprising 228 high-resolution video clips totaling 62,700 frames, with semantic annotations of weather and illumination conditions, scene environment, and camera-viewpoint motion. The clips are stratified into low-, medium-, and high-motion strata using optical-flow-based motion intensity. MotionScape supports Text2World, Image2World, and Video2World evaluation under a unified future-generation protocol. Technical validation with representative baseline models assesses performance across conditioning settings and motion strata, providing a unified platform for evaluating the future visual simulation capabilities of UAV world models.