FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance

arXiv 2026 30.8 method

TLDR

FlashMotion introduces a training framework to distill trajectory-controllable video generators into few-step models, plus FlashBench, improving quality and trajectory accuracy.

Reasoning

The paper addresses computational inefficiency in multi-step trajectory-controllable video generation with a distillation and adapter finetuning strategy, and contributes a benchmark. However, the abstract does not provide full experimental details or limitations, and it does not frame the work as a world model.

Read-first score

Read-first score 30.8, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 0.

Recency 8%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=benchmark,evaluation,experiment,result

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 42%

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 607.

Keyword Scores

world model

world simulator

generative world model

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper