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MoRE method enables articulated 3D object generation from single images

Researchers have introduced MoRE, a novel method for generating articulated 3D objects from single images. This approach encodes joint attributes using axis classification and Plücker moment coefficients, allowing for the reconstruction of object motion without arbitrary origin choices. MoRE's learning process compares decoded joint hypotheses with annotated joints to ensure motion preservation, and it utilizes an image-conditioned flow and a multi-state-adapted Gaussian model for functional latents and appearance. The system can articulate a single set of rest-pose Gaussians for different states, demonstrating improved part-box agreement and articulated-state geometry compared to existing methods. AI

IMPACT Introduces a new technique for generating articulated 3D objects, potentially improving applications in computer graphics and robotics.

RANK_REASON This is a research paper detailing a new method for 3D generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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MoRE method enables articulated 3D object generation from single images

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This is a research paper detailing a new method for 3D generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Guantian Zheng, Shan Jin ·

    MoRE: Motion-Regularized Encoding for Articulated 3D Generation

    arXiv:2610.30337v1 Announce Type: new 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 r…