Articulated 3D objects are central to many applications in robotics, AR/VR, and animation. Recent approaches to modeling such objects either rely on optimization-based reconstruction pipelines that require dense-view supervision or on feed-forward generative models that produce coarse geometric approximations and often overlook surface texture. In contrast, open-world 3D generation of static objects has achieved remarkable success, especially with the advent of native 3D diffusion models such as Trellis. However, extending these methods to articulated objects by training native 3D diffusion models poses significant challenges. In this work, we present FreeArt3D, a training-free framework for articulated 3D object generation. Instead of training a new model on limited articulated data, FreeArt3D repurposes a pre-trained static 3D diffusion model (e.g., Trellis) as a powerful shape prior. It extends Score Distillation Sampling (SDS) into the 3D-to-4D domain by treating articulation as an additional generative dimension. Given a few images captured in different articulation states, FreeArt3D jointly optimizes the object's geometry, texture, and articulation parameters without requiring task-specific training or access to large-scale articulated datasets. Our method generates high-fidelity geometry and textures, accurately predicts underlying kinematic structures, and generalizes well across diverse object categories. Despite following a per-instance optimization paradigm, FreeArt3D completes in minutes and significantly outperforms prior state-of-the-art approaches in both quality and versatility.

翻译：铰接式3D物体是机器人技术、增强现实/虚拟现实（AR/VR）以及动画领域中众多应用的核心。当前建模此类物体的方法主要依赖于两种途径：一是需要密集多视角监督的基于优化的重建流程，二是前馈式生成模型，这类模型往往仅能生成粗略的几何近似且常忽略表面纹理。相比之下，静态物体的开放世界3D生成已取得显著成功，特别是随着Trellis等原生3D扩散模型的出现。然而，通过训练原生3D扩散模型将这些方法扩展到铰接物体领域面临重大挑战。本文提出FreeArt3D，一种用于铰接3D物体生成的无训练框架。该方法无需在有限的铰接数据上训练新模型，而是将预训练的静态3D扩散模型（如Trellis）重新用作强大的形状先验。通过将铰接运动视为额外的生成维度，FreeArt3D将分数蒸馏采样（SDS）扩展至3D到4D领域。给定在少数不同铰接状态下捕获的图像，FreeArt3D能够联合优化物体的几何结构、纹理及铰接参数，而无需进行任务特定训练或依赖大规模铰接数据集。我们的方法能够生成高保真度的几何与纹理，准确预测底层的运动学结构，并在多样化的物体类别上展现出良好的泛化能力。尽管遵循逐实例优化的范式，FreeArt3D仅需数分钟即可完成生成，并在生成质量与多功能性方面显著超越了现有的先进方法。