MotionAtlas: A High-Quality Dataset and Benchmark for Dense Motion Captioning

We propose MotionAtlas, a system for detailed captioningof motion-centric videos, comprising (1) a dedicated human-annotatedbenchmark, (2) a scalable, high-quality pipeline to construct trainingsamples, and (3) a family of powerful Video-MLLMs. Unlike conventionalglobal motion captioning datasets, we focus on region-aware motion cap-tioning: given a video and a spatiotemporal mask, the model generatesprecise descriptions of motion within the target region, thereby alleviatingvisual clutter and motion entanglement and enabling reliable, quantifi-able evaluation. Concretely, we first build MotionAtlas-Bench, a compre-hensive benchmark comprising 2,073 multiple-choice questions, meticu-lously annotated for a curated set of high-quality, motion-centric videos,to evaluate fine-grained motion understanding of the objects in question.Second, we design a rigorous and scalable data pipeline that leveragesself-bootstrap refinement to suppress fine-grained hallucinations, yield-ing 159k high-quality motion captioning data. Third, we design a tai-lored training data composition strategy, which achieves consistent andsubstantial performance gains across diverse baseline Video-MLLMs, in-cluding Molmo2 and Qwen3-VL. For instance, MotionAtlas-4B surpassesQwen3-VL-4B by an average of 5.2 percentage points across general mo-tion benchmarks. The benchmark, dataset, and code have been released.