Toward Uncertainty-Aware, Physics-Grounded Human World Models
Overview
This paper proposes a framework to couple two existing components for building uncertainty-aware, physics-grounded human world models. The first component, KNOWN, reconstructs 3D human bodies in real time while estimating aleatoric and epistemic uncertainty, reducing error on low-density inputs from 81.3 mm to 70.3 mm. The second, PhysMoP, predicts future body configurations by solving Euler-Lagrange equations with learned dynamics, reducing prediction error from 9.6 mm to 2.1 mm on Human3.6M and from 12.1 mm to 0.7 mm on 3DPW. The authors define an interface between the two components, propose three mechanisms for integrating KNOWN's uncertainty into PhysMoP, and specify an evaluation protocol on in-the-wild video. No new experimental results are reported.
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Reported quantity · uncertainty-aware inference time: 11.6 other · Basis not reported
“certainty in real time (11.6 ms per frame); its aleatoric term identifies unreliable observations, its epistemic term identifies inputs from low-density regions of the training data, and refinement driven by the epistemic term reduces the error on such inputs from 81.3 to 70.3 mm. PhysMoP [2] predicts future body configuration by solving the Euler-Lagrange equations of the body with learned dynamics terms, reducing t”
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Reported quantity · prediction inference time: 80 other · Basis not reported
“he 80 ms prediction error from 9.6 to 2.1 mm on Human3.6M and from 12.1 to 0.7 mm on 3DPW. We define the interface between the two, propose three mechanisms by which KNOWN's uncertainty enters PhysMoP, and specify an evaluation protocol on in-the-wild video. No new experimental results are reported here.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalToward Uncertainty-Aware, Physics-Grounded Human World Models
This paper proposes a framework to couple two existing components for building uncertainty-aware, physics-grounded human world models. The first component, KNOWN, reconstructs 3D human bodies in real time while estimating aleatoric and epistemic uncertainty, reducing error on low-density inputs from 81.3 mm to 70.3 mm. The second, PhysMoP, predicts future body configurations by solving Euler-Lagrange equations with learned dynamics, reducing prediction error from 9.6 mm to 2.1 mm on Human3.6M and from 12.1 mm to 0.7 mm on 3DPW. The authors define an interface between the two components, propose three mechanisms for integrating KNOWN's uncertainty into PhysMoP, and specify an evaluation protocol on in-the-wild video. No new experimental results are reported.
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