MHPR: Multidimensional Human Perception and Reasoning Benchmark for Large Vision-Languate Models
Multidimensional human understanding is essential for real-world applications such as film analysis and virtual digital humans, yet current LVLM benchmarks largely focus on single-task settings and lack fine-grained, human-centric evaluation. In this work, we introduce MHPR, a comprehensive benchmark for joint perception-reasoning over human-centric scenes spanning individual, multi-person, and human-object interaction dimensions. MHPR comprises a multi-level data design-Captioned Raw Data (C-RD), Supervised Fine-Tuning Data (SFT-D), Reinforcement Learning Data (RL-D), and Test Data (T-D)-together with an automated caption/VQA generation pipeline (ACVG) that performs category-wise attribu...
| Authors | Kangkang Wang, Qinting Jiang, Wanping Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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