HumanPhys: A Human-Centric Physical-World Perception Benchmark for Real-World Visual Reasoning

Key points

• Each question targets a physically grounded quantity related to people, including depth, inter-person distance, gaze angle, displacement, velocity, heading-step trajectory, blink count, and contact-deformation strength.1
• The rendered probes additionally test illumination inference from body and face cues and camera ego-motion, both with and without a person in the frame.1
• HumanPhys systematically probes six dimensions of human-centric physical perception: spatial state, kinematics, blink events, deformation, illumination, and camera pose.1
• The six dimensions appear only partially dissociable across the tested MLLMs. We benchmark a range of open- and closed-source MLLMs on HumanPhys and find that all six capability dimensions remain challenging for current models.1

Sources (1)

• [1]HumanPhys: A Human-Centric Physical-World Perception Benchmark for Real-World Visual Reasoning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 07:50 AM

  We introduce HumanPhys, a human-centric benchmark for real-world physical perception, and a training corpus emitted by the same pipeline.

  Each question targets a physically grounded quantity related to people, including depth, inter-person distance, gaze angle, displacement, velocity, heading-step trajectory, blink count, and contact-deformation strength.

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