PartiGen: Part-Aware Representations for Language-Conditione
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: PartiGen: Part-Aware Representations for Language-Conditioned Humanoid Motion Generation. This paper presents PartiGen, a part-aware latent diffusion framework that integrates anatomical structure into whole-body motion representations. The model generates motion primitives autoregressively based on text and …
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Reported quantity · number of generated references: 500 other · Basis not reported
“l FID of 3.165, a 16.8% reduction over TextOp, and improves R@1 by 1.4 percentage points. In the duration study with 16-frame primitives, endpoint prediction reduces mean absolute duration error from 12.471 to 3.080 frames compared with a fixed-length cutoff. We further evaluate robot execution using a pretrained, frozen GRIT tracker: 409 of 500 generated references (81.8%) pass the posture-based stability screen in”
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Reported quantity · number of references passing stability screen: 409 other · Basis not reported
“l FID of 3.165, a 16.8% reduction over TextOp, and improves R@1 by 1.4 percentage points. In the duration study with 16-frame primitives, endpoint prediction reduces mean absolute duration error from 12.471 to 3.080 frames compared with a fixed-length cutoff. We further evaluate robot execution using a pretrained, frozen GRIT tracker: 409 of 500 generated references (81.8%) pass the posture-based stability screen in”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsPartiGen: Part-Aware Representations for Language-Conditioned Humanoid Motion Generation
This paper presents PartiGen, a part-aware latent diffusion framework that integrates anatomical structure into whole-body motion representations. The model generates motion primitives autoregressively based on text and motion history, achieving a segment-level FID of 3.165 and improved R@1 scores. It also demonstrates successful robot execution on real-world hardware.
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