← 研踪 PaperTrailCVF · CVPR 2026:cvf:ae8b43838aceff329e5925d43790b3d8 · 记录 #1
MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos
Gong, KehongWen, ZhengyuHe, WeixiaXu, MingxiWang, QiZhang, NingLi, ZhengyuLian, DongzeZhao, WeiHe, XiaoyuZhang, Mingyuan 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-02 00:39
摘要
Motion capture now underpins content creation far beyond digital humans, yet most pipelines remain species- or template-specific. We formalize this gap as Category-Agnostic Motion Capture (CAMoCap): given a monocular video and an arbitrary rigged 3D asset as a prompt, the goal is to reconstruct a rotation-based animation (e.g., BVH) that directly drives the specific asset. We present MoCapAnything, a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware Inverse Kinematics (IK) Fitting. MoCapAnything comprises three learnable modules and a lightweight IK stage: a Reference Prompt Encoder that distills per-joint queries from the asset's skeleton, mesh, and rendered image set; a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the modality gap between RGB tokens and the point-cloud-like joint space; and a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1,038 motion clips, each providing a standardized skeleton-mesh-rendered-video triad. Experiments on in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits non-trivial cross-species retargeting across heterogeneous rigs, offering a scalable path toward prompt-based 3D motion capture for arbitrary assets. The codes are available on our project page: https://animotionlab.github.io/MoCapAnything/
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos
Motion capture now underpins content creation far beyond digital humans, yet most pipelines remain species- or template-specific. We formalize this gap as Category-Agnostic Motion Capture (CAMoCap): given a monocular video and an arbitrary rigged 3D asset as a prompt, the goal is to reconstruct a rotation-based animation (e.g., BVH) that directly drives the specific asset. We present MoCapAnything, a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware Inverse Kinematics (IK) Fitting. MoCapAnything comprises three learnable modules and a lightweight IK stage: a Reference Prompt Encoder that distills per-joint queries from the asset's skeleton, mesh, and rendered image set; a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the modality gap between RGB tokens and the point-cloud-like joint space; and a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1,038 motion clips, each providing a standardized skeleton-mesh-rendered-video triad. Experiments on in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits non-trivial cross-species retargeting across heterogeneous rigs, offering a scalable path toward prompt-based 3D motion capture for arbitrary assets. The codes are available on our project page: https://animotionlab.github.io/MoCapAnything/
查看此版本解读 →查看此版本原文 ↗