PAct: 分解式单视角关节物体生成
PAct: Part-Decomposed Single-View Articulated Object Generation
AI总结：
PAct提出一种基于部分的生成框架，通过单视角图像生成高保真的关节物体，实现快速推理和可控组装，提升交互应用的效率与准确性。
摘要：
关节物体是交互式3D应用的核心，包括具身AI、机器人和VR/AR，其中功能部分分解和运动学运动是必不可少的。然而，生成高保真的关节资产仍然难以扩展，因为它需要可靠的部件分解和运动学绑定。现有方法主要分为两种范式：基于优化的重建或蒸馏，这些方法可以准确但通常每个实例需要数十分钟到数小时的时间，以及推理时间方法，依赖于模板或部分检索，产生合理的结果，但可能无法匹配输入观察中的特定结构和外观。我们引入了一个以部分为中心的生成框架，用于关节物体的创建，该框架在显式部分感知条件下合成部分几何、组成和关节运动。我们的表示模型将物体视为一组可移动部件，每个部件通过带有部分身份和关节提示的潜在令牌进行编码。在单张图像条件下，模型生成具有关节的3D资产，这些资产在实例级对应性上得以保持，同时保持有效的部分结构和运动。所提出的方法避免了每个实例的优化，实现了快速的前向推理，并支持可控的组装和关节运动，这对于具身交互至关重要。在常见的关节类别（如抽屉和门）上的实验表明，与基于优化和检索驱动的基线相比，输入一致性、部分准确性和关节合理性有所提高，同时显著减少了推理时间。
英文摘要：
Articulated objects are central to interactive 3D applications, including embodied AI, robotics, and VR/AR, where functional part decomposition and kinematic motion are essential. Yet producing high-fidelity articulated assets remains difficult to scale because it requires reliable part decomposition and kinematic rigging. Existing approaches largely fall into two paradigms: optimization-based reconstruction or distillation, which can be accurate but often takes tens of minutes to hours per instance, and inference-time methods that rely on template or part retrieval, producing plausible results that may not match the specific structure and appearance in the input observation. We introduce a part-centric generative framework for articulated object creation that synthesizes part geometry, composition, and articulation under explicit part-aware conditioning. Our representation models an object as a set of movable parts, each encoded by latent tokens augmented with part identity and articulation cues. Conditioned on a single image, the model generates articulated 3D assets that preserve instance-level correspondence while maintaining valid part structure and motion. The resulting approach avoids per-instance optimization, enables fast feed-forward inference, and supports controllable assembly and articulation, which are important for embodied interaction. Experiments on common articulated categories (e.g., drawers and doors) show improved input consistency, part accuracy, and articulation plausibility over optimization-based and retrieval-driven baselines, while substantially reducing inference time.
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。
