AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation

TL;DR

AniGen is a novel framework that directly generates animatable 3D assets from a single image by representing shape, skeleton, and skinning as mutually consistent $S^3$ Fields, improving rig validity and animation quality.

Contribution

AniGen introduces a unified $S^3$ Field representation and a two-stage pipeline for directly generating animate-ready 3D assets conditioned on a single image.

Findings

Outperforms state-of-the-art baselines in rig validity and animation quality.

Generalizes effectively to diverse categories including animals, humanoids, and machinery.

Successfully generates animate-ready assets from in-the-wild images.

Abstract

Animatable 3D assets, defined as geometry equipped with an articulated skeleton and skinning weights, are fundamental to interactive graphics, embodied agents, and animation production. While recent 3D generative models can synthesize visually plausible shapes from images, the results are typically static. Obtaining usable rigs via post-hoc auto-rigging is brittle and often produces skeletons that are topologically inconsistent with the generated geometry. We present AniGen, a unified framework that directly generates animate-ready 3D assets conditioned on a single image. Our key insight is to represent shape, skeleton, and skinning as mutually consistent $S^3$ Fields (Shape, Skeleton, Skin) defined over a shared spatial domain. To enable the robust learning of these fields, we introduce two technical innovations: (i) a confidence-decaying skeleton field that explicitly handles the geometric ambiguity of bone prediction at Voronoi boundaries, and (ii) a dual skin feature field that decouples skinning weights from specific joint counts, allowing a fixed-architecture network to predict rigs of arbitrary complexity. Built upon a two-stage flow-matching pipeline, AniGen first synthesizes a sparse structural scaffold and then generates dense geometry and articulation in a structured latent space. Extensive experiments demonstrate that AniGen substantially outperforms state-of-the-art sequential baselines in rig validity and animation quality, generalizing effectively to in-the-wild images across diverse categories including animals, humanoids, and machinery. Homepage: https://yihua7.github.io/AniGen-web/