BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives
Summary
BLARM is a feed-forward method for animating 3D meshes from monocular video by blending latent rigid motion primitives, enabling temporally coherent animations without explicit rigs.
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Paper page - BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives
Source: https://huggingface.co/papers/2608.31113
Abstract
BLARM predicts temporally coherent 3D mesh animations from monocular video using learned rigid motion components and skinning weights without explicit rigs.
We introduce BLARM, afeed-forwardmethod forvideo-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a temporally coherent animated mesh whose motion follows the video. Rather than relying on explicit rigs or directly regressing high-dimensional vertex motion, we represent animation using a compact set of learned, time-varyingrigid motion componentsand time-invariant vertex-to-componentskinning weights. This yields a low-dimensional deformation space without requiring skeletons, cages,skinning weights, or rig annotations. Our architecture conditions geometry-deriveddeformation latentson video features throughfactorized spatial-temporal attention, then decodes rigid transformations blended by predictedskinning weights. Trained withtrajectory reconstruction,entropy regularization, andmotion-aware contrastive learning, BLARM produces accurate and temporally stable animations while recovering compact, interpretable motion structure from monocular video.
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