One model fills motion for cloth, robots, and beings without retraining
A single model fills in dense motion for varied shapes using a base plus a dimensionless residual.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Chih-Hai Su, Tsung-Wei Ke, Yu-Lun Liu
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Problem: matching motion across fabric and robots is hard with one model. Approach: separate motion size from shape, using a simple baseline motion plus a small learned adjustment that works across groups. Finding: it beats simple motion baselines and even works on new types without templates.
Why it matters
Takeaway: estimate motion from what is seen, not from fixed templates; this allows flexible use across devices without custom models.
The method uses a simple motion base plus a small learned adjustment that fits shapes like cloth or walking robots, with no fixed presets.
In a hypothetical case, a curtain moves by what you observe; the model scales the adjustment to fit that motion.
What remains uncertain
Performance with noisy data, sensor faults, or materials beyond tested cloth and legged bodies is still uncertain in varied conditions.
Original sources · 1
Check the original paper for its authors, methods, version and access terms.
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