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

TL;DR

Rigel3D is a novel generative approach that produces high-quality, rigged 3D assets with geometry and skeletons, enabling animation and retargeting for applications like gaming and film.

Contribution

It jointly models geometry and rig structure via coupled latent representations, introducing an autoencoder and a two-stage generative model for animation-ready assets.

Findings

Generates diverse, high-quality rigged 3D assets.

Outperforms existing rigging baselines on multiple metrics.

Supports open-vocabulary joint labeling for retargeting.

Abstract

Recent 3D generative models can synthesize high-quality assets, but their outputs are typically static: they lack the skeletal rigs, joint hierarchies, and skinning weights required for animation. This limits their use in games, film, simulation, virtual agents, and embodied AI, where assets must not only look plausible but also move plausibly. We introduce Rigel3D, a generative method for animation-ready 3D assets represented as rigged meshes. Unlike post-hoc auto-rigging methods that attach rigs to completed shapes, our method jointly models geometry and rig structure through coupled surface and skeleton structured latent representations. A rig-aware autoencoder decodes these representations into mesh geometry, skeleton topology, joint coordinates, and skinning weights, while a two-stage latent generative model synthesizes both surface and skeleton representations for image-conditioned generation. To support downstream animation workflows, we further introduce an open-vocabulary joint labeling module that embeds generated joints into a shared vision-language space, enabling correspondence to arbitrary retargeting templates. Experiments on large-scale rigged asset datasets demonstrate that our method generates diverse, high-quality animation-ready assets and outperforms existing rigging baselines across multiple metrics.