AnyAct generates editable human reenactments from character videos via conditional motion generation from transferable sparse local 2D articulated cues, with designs for human-only supervision and global-local decoupling.
Retargetting motion to new characters,
2 Pith papers cite this work. Polarity classification is still indexing.
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Human2Humanoid is an unsupervised motion retargeting framework using CycleGAN, skeleton-aware GCN, end-effector consistency loss, and physics-aware constraints to transfer human motions to humanoid robots without paired data.
citing papers explorer
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AnyAct: Towards Human Reenactment of Character Motion From Video
AnyAct generates editable human reenactments from character videos via conditional motion generation from transferable sparse local 2D articulated cues, with designs for human-only supervision and global-local decoupling.
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Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots
Human2Humanoid is an unsupervised motion retargeting framework using CycleGAN, skeleton-aware GCN, end-effector consistency loss, and physics-aware constraints to transfer human motions to humanoid robots without paired data.