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English(EN) From Digital Human Interactions to Physics-Based Humanoid Skills: Physics-Grounded Post-Training of Interaction Generators

新的DIGHT框架增强了模拟中拟人交互的生成

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DIGHT，一个旨在改善数字人之间交互生成的新框架。这个协同适应系统将交互生成器与拟人跟踪策略相结合。DIGHT首先模拟多个交互候选，然后使用从模拟运行中得出的基于物理的偏好，通过扩散直接偏好优化来改进生成器。然后，改进后的生成器微调跟踪策略，增强了生成运动与物理执行之间的兼容性，从而在模拟中产生更合理、更逼真的拟人交互。 AI

影响 增强了人形机器人和人工智能研究的模拟真实感。

排序理由 该集群包含一篇研究论文，详细介绍了用于人工智能驱动的拟人交互生成的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的DIGHT框架增强了模拟中拟人交互的生成

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该集群包含一篇研究论文，详细介绍了用于人工智能驱动的拟人交互生成的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Kerui Chen, Jianrong Zhang, Kai Lv, Hehe Fan ·

    从数字人交互到基于物理的拟人化技能：交互生成器的物理基础训练后训练

    arXiv:2610.10322v1 Announce Type: new Abstract: Recent methods have made promising progress in generating interactions between two humanoids, largely relying on physics-based tracking policies to convert digital reference motions into executable trajectories. However, limited tra…

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