Reproduce “Lifting Motion to the 3D World via 2D Diffusion” on OpenResearch
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About this paper
Jiaman Li, C. Karen Liu, Jiajun Wu
MVLift estimates global 3D motion, including joint rotations and root trajectories, solely from 2D pose sequences during training. This method demonstrates performance superior to some 3D-supervised baselines, particularly in root translation accuracy, and generalizes effectively to animal motion and human-object interactions.
Key insights
- Multi-view consistency, essential for 3D motion reconstruction, can be progressively inferred and learned solely from single-view 2D pose sequences.
- 2D motion diffusion models are effective at learning plausible motion priors, which are critical for resolving depth ambiguities in 3D lifting from 2D inputs.
- Synthetic generation of strictly geometrically consistent multi-view 2D data provides high-quality supervision for training an efficient multi-view diffusion model for 3D inference.
Summary by alphaXiv.
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