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arXiv · 2607.17769

From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation

Nabeela Khan·Bowen Wu·Runwu Shi·Benjamin Yen·Takeshi Ashizawa·Carlos Toshinori Ishi·Takashi Minato·Kazuhiro Nakadai
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Abstract

Recent sign language generation (SLG) systems increasingly output dense 3D body representations, which better preserve full-body kinematics and geometry for downstream embodiment on humanoid robots. However, these generated motions frequently exhibit self-intersections such as hand-hand and hand-torso penetration. While such artifacts may be tolerated in offline rendering, they become critical in humanoid execution as they lead to infeasible inverse-kinematics (IK) solutions, collisions, and unstable retargeted trajectories. We present a system-level framework that bridges SLG outputs to humanoid joint-space execution via two components. First, we introduce a volumetric SMPL-X collision-mitigation module that projects generated signing motions toward physically plausible configurations while minimally deviating from the original trajectory. Second, we propose a vision-language-guided retargeting algorithm built on an IK backbone: a VLM serves as a visual critic over rendered humanoid motion, identifies embodiment-specific failure modes, and triggers targeted task-space corrections. Our results highlight collision handling and perception-guided refinement as key missing components for reliable humanoid signing.

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Nabeela Khan, Bowen Wu, Runwu Shi, Benjamin Yen, Takeshi Ashizawa, Carlos Toshinori Ishi, Takashi Minato, Kazuhiro Nakadai. 2026-07-20. From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation. https://arxiv.org/abs/2607.17769

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