Pith. sign in

REVIEW 5 cited by

RigNet: Neural Rigging for Articulated Characters

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2005.00559 v2 pith:CS3C2OEY submitted 2020-05-01 cs.GR cs.CV

Zhan Xu , Yang Zhou , Evangelos Kalogerakis , Chris Landreth , Karan Singh This is my paper
classification cs.GRcs.CV
keywords rigsrignetanimatorarchitecturearticulatedcharacterinputmesh
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet predicts a skeleton that matches the animator expectations in joint placement and topology. It also estimates surface skin weights based on the predicted skeleton. Our method is based on a deep architecture that directly operates on the mesh representation without making assumptions on shape class and structure. The architecture is trained on a large and diverse collection of rigged models, including their mesh, skeletons and corresponding skin weights. Our evaluation is three-fold: we show better results than prior art when quantitatively compared to animator rigs; qualitatively we show that our rigs can be expressively posed and animated at multiple levels of detail; and finally, we evaluate the impact of various algorithm choices on our output rigs.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ViP-Rig: Visual-Prompted Controllable Rigging

    cs.CV 2026-07 conditional novelty 6.0 of 10

    2D skeletal sketches and rigidity maps injected via gated adapters into frozen UniRig and Puppeteer backbones recover target rigs better than geometry-only baselines and support iterative editing.

  2. MorphGS: Morphology-Adaptive Articulated 3D Motion Transfer from Videos

    cs.CV 2026-01 conditional novelty 6.0 of 10

    MorphGS retargets motion from a monocular video onto a rigged 3D character by optimizing target morphology and pose with image-space losses, without 3D source reconstruction or parametric templates.

  3. VidAnimator: User-Guided Stylized 3D Character Animation from Human Videos

    cs.HC 2025-08 conditional novelty 6.0 of 10

    A mixed-initiative system combining video motion capture with editable skinning-weight transfer lets stylized 3D characters mimic human videos.

  4. Category-Agnostic Neural Object Rigging

    cs.CV 2025-05 conditional novelty 6.0 of 10

    An unsupervised method that represents deformable 3D objects as sparse, editable blobs and re-poses them across categories without manual rigging.

  5. PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling

    cs.CV 2025-06 reject novelty 5.0 of 10

    PhysRig animates articulated 3D objects by simulating them as deformable soft bodies driven by an embedded skeleton, and learns the material and motion parameters with a differentiable physics simulator.

Pith tools