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Particulate: Feed-Forward 3D Object Articulation.arXiv preprint arXiv:2512.11798

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

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2026 4

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UNVERDICTED 4

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representative citing papers

Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation

cs.GR · 2026-05-13 · unverdicted · novelty 8.0

Rigel3D jointly generates rigged 3D meshes with geometry, skeleton topology, joint positions, and skinning weights using coupled surface and skeleton latent representations for image-conditioned animation-ready asset synthesis.

ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes

cs.CV · 2026-04-19 · unverdicted · novelty 7.0 · 2 refs

ViPS learns a universal, controllable pose space for auto-rigged meshes by transferring motion priors from video diffusion models, matching SOTA performance on plausibility and diversity while enabling zero-shot generalization.

3D Generation for Embodied AI and Robotic Simulation: A Survey

cs.RO · 2026-04-29 · unverdicted · novelty 2.0 · 3 refs

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.

citing papers explorer

Showing 4 of 4 citing papers.

  • Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation cs.GR · 2026-05-13 · unverdicted · none · ref 5

    Rigel3D jointly generates rigged 3D meshes with geometry, skeleton topology, joint positions, and skinning weights using coupled surface and skeleton latent representations for image-conditioned animation-ready asset synthesis.

  • ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes cs.CV · 2026-04-19 · unverdicted · none · ref 16 · 2 links

    ViPS learns a universal, controllable pose space for auto-rigged meshes by transferring motion priors from video diffusion models, matching SOTA performance on plausibility and diversity while enabling zero-shot generalization.

  • AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation cs.GR · 2026-04-09 · unverdicted · none · ref 15

    AniGen directly generates animatable 3D assets with consistent shape, skeleton, and skinning from single images using unified S^3 fields and a two-stage flow-matching pipeline.

  • 3D Generation for Embodied AI and Robotic Simulation: A Survey cs.RO · 2026-04-29 · unverdicted · none · ref 81 · 3 links

    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.