MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos
Motion capture now underpins content creation far beyond digital humans, yet most existing 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 such as 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. The system contains three learnable modules and a lightweight IK stage: (1) a Reference Prompt Encoder that extracts per-joint queries from the asset's skeleton, mesh, and rendered images; (2) a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the gap between video and joint space; and (3) a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1038 motion clips, each providing a standardized skeleton-mesh-render triad. Experiments on both in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits meaningful cross-species retargeting across heterogeneous rigs, enabling scalable, prompt-driven 3D motion capture for arbitrary assets. Project page: https://animotionlab.github.io/MoCapAnything/
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Kehong Gong
admin_assigned
2025-12-12T13:07:11.844Z
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Gongkehong
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kehong
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Zhengyu Wen
admin_assigned
2025-12-12T13:07:21.767Z
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Zhengyu Wen
wzy27
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wzy27
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Weixia He
admin_assigned
2025-12-12T13:07:32.488Z
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He Weixia
weixia111111
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weixia111111
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Mingxi Xu
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Qi Wang
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Ning Zhang
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Zhengyu Li
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Dongze Lian
admin_assigned
2025-12-12T13:07:42.284Z
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Dongze Lian
DonaldLian
user
DonaldLian
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Wei Zhao
693b87039874a2a5e4ffb2fe
Xiaoyu He
693b87039874a2a5e4ffb2ff
Mingyuan Zhang
MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos
Motion capture now underpins content creation far beyond digital humans, yet most existing 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 such as 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. The system contains three learnable modules and a lightweight IK stage: (1) a Reference Prompt Encoder that extracts per-joint queries from the asset's skeleton, mesh, and rendered images; (2) a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the gap between video and joint space; and (3) a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1038 motion clips, each providing a standardized skeleton-mesh-render triad. Experiments on both in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits meaningful cross-species retargeting across heterogeneous rigs, enabling scalable, prompt-driven 3D motion capture for arbitrary assets. Project page: https://animotionlab.github.io/MoCapAnything/
MoCapAnything is a reference-guided framework that reconstructs rotation-based animations from monocular video for arbitrary rigged 3D assets, enabling cross-species retargeting and scalable 3D motion capture.
Category-Agnostic Motion Capture CAMoCap MoCapAnything Reference Prompt Encoder Video Feature Extractor Unified Motion Decoder 3D joint trajectories inverse kinematics BVH Truebones Zoo skeletal animations cross-species retargeting