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English(EN) IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model

新的IKMo框架根据图像生成具有多样化约束的运动

研究人员推出了一种新颖的框架IKMo,用于从图像生成运动,解决了整合多样化空间约束的挑战。该系统利用TPMDM,一种具有并行控制策略的扩散模型,来解耦和对齐来自2D图像的全局轨迹信息与局部姿态数据。该方法旨在通过允许用户使用未对齐的、现实世界中的图像进行工作,从而使运动生成更加易于访问,并在HumanML3D和KIT-ML等基准测试上建立了新的最先进水平。 AI

影响 这项研究可能为使用未对齐图像数据的用户带来更易于访问和更精确的动画工具。

排序理由 该集群包含一篇详细介绍运动生成新模型和框架的研究论文。[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IKMo框架根据图像生成具有多样化约束的运动

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该集群包含一篇详细介绍运动生成新模型和框架的研究论文。[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) · Yang Zhao, Yan Zhang, Zixuan Guo, Hangyu Zhou, Haotian Mao, Hui Wang, Xubo Yang ·

    IKMo:具有轨迹-姿态条件运动扩散模型的图像关键帧运动生成

    arXiv:2505.21146v2 Announce Type: replace-cross Abstract: While text-to-motion generation is mainstream, integrating explicit spatial constraints has become a standard solution for precise animation. Existing state-of-the-art models are tailored for professional workflows, effect…