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arXiv · 2411.18808

Lifting Motion to the 3D World via 2D Diffusion

Jiaman Li·C. Karen Liu·Jiajun Wu
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Abstract

Estimating 3D motion from 2D observations is a long-standing research challenge. Prior work typically requires training on datasets containing ground truth 3D motions, limiting their applicability to activities well-represented in existing motion capture data. This dependency particularly hinders generalization to out-of-distribution scenarios or subjects where collecting 3D ground truth is challenging, such as complex athletic movements or animal motion. We introduce MVLift, a novel approach to predict global 3D motion -- including both joint rotations and root trajectories in the world coordinate system -- using only 2D pose sequences for training. Our multi-stage framework leverages 2D motion diffusion models to progressively generate consistent 2D pose sequences across multiple views, a key step in recovering accurate global 3D motion. MVLift generalizes across various domains, including human poses, human-object interactions, and animal poses. Despite not requiring 3D supervision, it outperforms prior work on five datasets, including those methods that require 3D supervision.

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Jiaman Li, C. Karen Liu, Jiajun Wu. 2024-11-27. Lifting Motion to the 3D World via 2D Diffusion. https://arxiv.org/abs/2411.18808

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