English(EN) Robust Motion Generation using Part-level Reliable Data from Videos

新方法利用可信身体部位从视频生成角色动作

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 04:00

研究人员开发了一种新颖的从视频生成运动的方法，该方法侧重于人体中清晰可见的“可信”部位。该方法使用部件感知掩码自回归模型来预测缺失或被遮挡的身体部位，从而提高生成运动序列的质量和多样性。该团队还推出了 K700-M，这是一个包含约 200,000 个真实世界运动序列的新基准数据集，用于评估此类模型。 AI

影响这项研究可能为角色动画带来更具可扩展性和多样性的数据集，从而可能提高虚拟环境和游戏中文成运动的质量。

排序理由该集群包含一篇详细介绍运动生成新方法和基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍运动生成新方法和基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Boyuan Li, Sipeng Zheng, Bin Cao, Ruihua Song, Zongqing Lu · 2026-08-26 04:00

利用视频中的部件级可靠数据生成鲁棒运动

arXiv:2512.12703v2 Announce Type: replace-cross Abstract: Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames cannot be seen due to off-screen captures or …

本文如何被排名

报道来源 [1]

利用视频中的部件级可靠数据生成鲁棒运动

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