UnfoldArt uses a two-round structured debate between high-level semantic agents and low-level parameter agents, grounded in generated video, to infer articulation and reconstruct full articulated 3D objects including occluded geometry from text or image inputs.
Dreamart: Generating interactable articulated objects from a single image
6 Pith papers cite this work. Polarity classification is still indexing.
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PWM-ArtGen couples action and image diffusion models for joint learning of dynamics and kinematics on a new 19.7k dataset, outperforming baselines with zero-shot generalization to out-of-distribution articulated objects.
PhysX-Omni unifies simulation-ready 3D asset generation across rigid, deformable, and articulated objects via a new geometry representation, the PhysXVerse dataset, and the PhysX-Bench evaluation suite.
QueST replaces local point tracking with persistent semantic queries that globally attend to spatio-temporal features and apply 3D grounding to suppress drift, cutting absolute point error by 67.7% versus TAP-Net on long articulated sequences.
SPAGS reconstructs articulated objects from sparse single-state RGB images by constraining Gaussians to planar primitives, optimizing with depth and diffusion priors, and using a VLM for part segmentation and joint estimation.
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and transfer.
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3D Generation for Embodied AI and Robotic Simulation: A Survey
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and transfer.