SPARK: 面向模拟的关节化重建与VLK知识
SPARK: Sim-ready Part-level Articulated Reconstruction with VLM Knowledge
AI总结：
SPARK通过结合VLK和生成扩散模型，实现从单张图像中生成物理一致的关节化3D物体，提升机器人操作和交互建模的应用效果。
摘要：
关节化3D物体对于具身AI、机器人和交互场景理解至关重要，但创建模拟准备的资产仍然费力且需要专家对部分层次和运动结构进行建模。我们介绍了SPARK，一种从单张RGB图像中重建物理一致、运动学部分级关节化物体的框架。给定输入图像，我们首先利用VLK提取粗略URDF参数并生成部分级参考图像。然后将部分图像指导和推断的结构图整合到生成扩散变压器中，以合成一致的部分和完整的关节化物体形状。为进一步优化URDF参数，我们结合可微前向运动学和可微渲染，在VLK生成的开放状态监督下优化关节类型、轴和原点。大量实验表明，SPARK在多样化类别中生成高质量的模拟准备关节化资产，使下游应用如机器人操作和交互建模成为可能。项目页面：https://heyumeng.com/SPARK/index.html。
英文摘要：
Articulated 3D objects are critical for embodied AI, robotics, and interactive scene understanding, yet creating simulation-ready assets remains labor-intensive and requires expert modeling of part hierarchies and motion structures. We introduce SPARK, a framework for reconstructing physically consistent, kinematic part-level articulated objects from a single RGB image. Given an input image, we first leverage VLMs to extract coarse URDF parameters and generate part-level reference images. We then integrate the part-image guidance and the inferred structure graph into a generative diffusion transformer to synthesize consistent part and complete shapes of articulated objects. To further refine the URDF parameters, we incorporate differentiable forward kinematics and differentiable rendering to optimize joint types, axes, and origins under VLM-generated open-state supervision. Extensive experiments show that SPARK produces high-quality, simulation-ready articulated assets across diverse categories, enabling downstream applications such as robotic manipulation and interaction modeling. Project page: https://heyumeng.com/SPARK/index.html.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
