Infrared and visible image fusion integrates complementary information from both modalities to provide a more comprehensive representation of the scene. We propose ProtoFusion, a protocol-guided generative framework for infrared and visible image fusion. Instead of relying on fixed fusion operators, hand-crafted objectives, or manually specified one-shot prompts, ProtoFusion formulates fusion as protocol-constrained generation. It represents fusion intent with a structured protocol, consisting of environment state cards, grounding principles, and a style profile, which explicitly specifies which evidence should be preserved or suppressed and which properties the fused result should satisfy. To optimize this protocol, we introduce protocol-guided prompt refinement, a closed-loop learning framework that iteratively performs generation, evaluation, and revision under multimodal evidence. Once learned, the optimized protocol is fixed at inference time and directly guides fusion generation. To address residual source inconsistencies, we further incorporate structured belief construction, which organizes modality reliability, scene evidence, and cross-modal conflicts into a structured representation for conservative correction after generation. Extensive experiments on standard benchmarks show that ProtoFusion improves quantitative performance, achieves favorable results in aesthetic evaluation, and produces visually coherent fusion outputs. Project Page: {https://erfect2020.github.io/ProtoFusion/}.
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.