pith:VNNROT63
R-DMesh: Video-Guided 3D Animation via Rectified Dynamic Mesh Flow
A learned rectification jump offset aligns arbitrary input mesh poses to video starting frames before animation.
arxiv:2605.13838 v2 · 2026-05-13 · cs.CV · cs.GR · cs.LG
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\pithnumber{VNNROT63JR6ARBJIIJCYZOLK3R}
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Record completeness
Claims
our method introduces a novel VAE that explicitly disentangles the input into a conditional base mesh, relative motion trajectories, and a crucial rectification jump offset. This offset is learned to automatically transform the arbitrary pose of the input mesh to match the video's initial state before animation begins.
That a learned rectification jump offset can reliably map arbitrary input mesh poses onto the video's starting frame without introducing geometric distortion or breaking downstream physical consistency enforced by Triflow Attention.
R-DMesh generates high-fidelity 4D meshes aligned to video by disentangling base mesh, motion, and a learned rectification jump offset inside a VAE, then using Triflow Attention and rectified-flow diffusion.
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Receipt and verification
| First computed | 2026-05-18T02:44:14.853411Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ab5b174fdb4c7c08852842458cb96adc495fc2940cdae784767b1fef7583c2e1
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/VNNROT63JR6ARBJIIJCYZOLK3R \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ab5b174fdb4c7c08852842458cb96adc495fc2940cdae784767b1fef7583c2e1
Canonical record JSON
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"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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"submitted_at": "2026-05-13T17:58:13Z",
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