[Submitted on 21 Oct 2026]
Title:MoRE: Motion-Regularized Encoding for Articulated 3D Generation
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Abstract:Generating an articulated object from a single image requires recovering both its appearance and the mechanism governing its parts. A compact encoding of joint attributes does not by itself ensure that reconstruction preserves the resulting motion. We present MoRE, Motion-Regularized Encoding for articulated 3D generation. MoRE represents coordinate-aligned joint lines with axis classification and two axis-orthogonal Plücker moment coefficients, removing the arbitrary choice of an origin along the axis. During representation learning, decoded joint hypotheses are used to move corresponding three-dimensional anchors, and their motions are compared with those of annotated joints. A differentiable expectation over joint types and axis directions couples attribute reconstruction to the motion it induces. An image-conditioned flow learns the resulting functional latents, while a multi-state-adapted Gaussian model supplies appearance. At inference, recovered part assignments and joints articulate a single set of rest-pose Gaussians without regenerating the object for each state. Experiments on PartNet-Mobility and ACD show stronger part-box agreement, articulated-state geometry and visual similarity than the compared baselines, while joint prediction reduces axis, origin and range errors relative to ArtiLatent. Controlled ablations support the joint-line encoding and show that motion supervision reduces anchor-motion error. Code and pretrained checkpoints will be released in the future.
Comments: 6 pages, 3 figures, 4 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.30337 [cs.CV]
(or arXiv:2610.30337v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.30337
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Guantian Zheng [view email] [v1] Wed, 21 Oct 2026 18:55:12 UTC (3,238 KB)
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