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ScaleMoGen framework advances text-driven human motion generation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ScaleMoGen, a novel framework for generating human motion from text descriptions. This approach utilizes an autoregressive method that predicts motion tokens across multiple scales, from coarse to fine, ensuring the preservation of skeletal hierarchy and motion details. ScaleMoGen demonstrates state-of-the-art performance on benchmarks like HumanML3D and SnapMoGen, outperforming existing methods such as MoMask and MoMask++ in terms of FID and CLIP Score. The framework also enables efficient, training-free editing of generated motions. AI

IMPACT This research advances generative models for complex 3D data, potentially impacting animation, gaming, and virtual reality.

RANK_REASON The cluster contains a research paper detailing a new method for human motion generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ScaleMoGen framework advances text-driven human motion generation

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The cluster contains a research paper detailing a new method for human motion generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Inwoo Hwang, Hojun Jang, Bing Zhou, Jian Wang, Young Min Kim, Chuan Guo ·

    ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

    arXiv:2605.11704v2 Announce Type: replace Abstract: We present ScaleMoGen, a scale-wise autoregressive framework for text-driven human motion generation. Unlike conventional autoregressive approaches that rely on standard next-token prediction, ScaleMoGen frames motion generation…

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