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English(EN) BiMoGen: Bidirectional Motion-Text Generation via Unified Masked Discrete Diffusion

BiMoGen框架使用扩散实现双向运动-文本生成

研究人员开发了BiMoGen,一个利用掩码离散扩散的双向运动-文本生成新框架。该方法通过实现迭代式双向预测,克服了先前自回归模型的局限性,从而更好地捕捉语言和运动之间的依赖关系。该框架包含一个两阶段的训练过程,包括解耦的单模态和跨模态训练以实现初始对应,以及生成感知的自纠正以在推理过程中优化预测。在HumanML3D和KIT-ML数据集上的实验表明,BiMoGen在运动到文本描述和文本到运动生成方面均取得了有竞争力的性能。 AI

影响 这项研究为文本到运动和运动到文本生成引入了一种新方法,有望改进动画、游戏和人机交互等领域的应用。

排序理由 该条目描述了一篇介绍特定AI任务新框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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

BiMoGen框架使用扩散实现双向运动-文本生成

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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    BiMoGen:通过统一掩码离散扩散实现双向运动-文本生成

    Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existing unified approaches mostly rely on autoregressive modeling, which imposes a fixed generation order …