无需配准的3D人体扫描也能精准预测和迁移动作。
ScanMove: Motion Prediction and Transfer for Unregistered Body Meshes
- 用特征场+运动嵌入网络生成时空形变场驱动变形
- 在未配准扫描上实现步行跑步等动作的高质量迁移
- 适合处理带噪原始扫描，无需点对应关系
未经配准的表面网格，尤其是原始3D扫描，由于缺乏点对点对应关系且数据含噪，给自动计算合理形变带来巨大挑战。本文提出一种无骨架、数据驱动的新框架，用于此类人体网格的动作预测与迁移。方法结合鲁棒的运动嵌入网络与学习得到的逐顶点特征场，生成时空形变场以驱动网格变形。大量评估包括定量基准测试和定性可视化，在步行、跑步等任务中证明了该方法在困难未配准网格上的有效性与通用性。
原文摘要 · Abstract (English)
Unregistered surface meshes, especially raw 3D scans, present significant challenges for automatic computation of plausible deformations due to the lack of established point-wise correspondences and the presence of noise in the data. In this paper, we propose a new, rig-free, data-driven framework for motion prediction and transfer on such body meshes. Our method couples a robust motion embedding network with a learned per-vertex feature field to generate a spatio-temporal deformation field, which drives the mesh deformation. Extensive evaluations, including quantitative benchmarks and qualitative visuals on tasks such as walking and running, demonstrate the effectiveness and versatility of our approach on challenging unregistered meshes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。