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This paper introduces ViP-Rig, a visual-prompted framework for controllable rigging that enhances the process of skeletal structure and deformation behavior editing in animation tasks. By utilizing user-drawn 2D prompts, ViP-Rig generates skeletons and skinning weights with a two-stage approach that combines visual prompt encoding with a frozen pretrained generator. Experimental results on Articulation-XL2.0 and zero-shot evaluation on ModelsResource show that ViP-Rig outperforms existing geometry-conditioned methods in accurately recovering target skeletons and skinning weights, providing artists with greater control over rigging outcomes.
ViP-Rig enables artists to achieve precise control over skeletal structures and skinning weights through intuitive visual prompts, outperforming traditional geometry-based methods.
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an initial rig and repeatedly edit its skeletal structure and deformation behavior to meet specific animation requirements. Existing automatic methods primarily generate a plausible rig from geometry, offering limited explicit control over the resulting skeleton and deformation behavior. In this work, we present ViP-Rig, a visual-prompted framework that supports both prompt-first rigging and result-guided editing by injecting features extracted from user-drawn or edited 2D skeletal and rigidity prompts into frozen pretrained backbones. Specifically, ViP-Rig consists of two stages, Skeleton Generation and Skinning Prediction. In the first stage, the skeletal sketch is processed by the Dense-to-Compact Visual Prompt Encoding to produce compact, fixed-length conditioning tokens. The resulting tokens are injected into a frozen pretrained autoregressive generator through gated adapters to control joint placement and branching structure while preserving the generator's geometric prior. In the second stage, the rigidity map is processed using the same visual encoding design, while the pretrained skinning backbone remains frozen. The resulting tokens are symmetrically injected into the point and joint streams to modulate point-joint compatibility and the resulting skinning weights. Experiments on Articulation-XL2.0 and zero-shot evaluation on ModelsResource show that ViP-Rig more accurately recovers target skeletons and skinning weights than geometry-conditioned baselines under prompt-guided evaluation. Qualitative results further demonstrate explicit and localized control in both prompt-first rigging and result-guided editing.
