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.
Particulate: Feed-Forward 3D Object Articulation.arXiv preprint arXiv:2512.11798
4 Pith papers cite this work. Polarity classification is still indexing.
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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 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.
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
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Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation
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.
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ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes
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.
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AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation
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.
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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.