HumanMoveVQA: Can Video MLLMs reason about human movement in videos?
Despite the rapid advance of Multimodal Large Language Models (MLLMs) in high-level video understanding, a fundamental bottleneck remains: these models collapse complex human motion into coarse semantic labels. Existing benchmarks mostly focus on scene-centric events or local joint articulations, failing to probe global human motion in space over time (trajectory and orientation changes). We introduce HumanMoveVQA, the first comprehensive benchmark designed to evaluate global trajectory and orientation reasoning from an exocentric perspective. Our benchmark utilizes a first-frame anchored world coordinate system, preserving translation and rotation relative to a fixed starting point. We p...
| Authors | Pulkit Gera, Faegheh Sardari, Asmar Nadeem et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 June 2026 |
| Last updated at source | 29 June 2026 |
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