AI 看一眼就能拆解物体关节
现在的 AI 看一张图，能认出这是椅子，但不知道哪块能动、怎么动。这篇让 AI 从几张不同角度的照片里，直接拆出物体的可动部件和关节参数——比如抽屉能拉多远、门能转多大角度。它不靠猜，而是把多张图的信息合起来推理，哪怕只有一张图也能工作。研究者还造了个自动生成训练数据的工具，让模型见过更多样的物体。它不是你明天能用上的东西，但这是机器人抓取、AR 交互里「看懂物体怎么动」的关键一步。
📄 原文摘要(英文)
Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Most feed-forward methods infer articulation from a single observation and therefore rely heavily on learned category-level shape priors. We present FAMOS, a feed-forward model that predicts movable-part segmentation and joint parameters from a sparse, unordered set of partial point clouds. Our model jointly reasons over multiple observations and naturally supports a variable number of inputs, including a single view. To aggregate articulation cues across observations, we introduce a Multi-state Articulation Transformer with alternating state-wise and global attention. We further propose an observed articulation span objective that supervises the motion range each part exhibits across the input observations, encouraging the model to leverage the full observation set. To overcome the limited scale and diversity of existing datasets, we introduce a procedural data generator that synthesizes self-annotated assets during training. Experiments on PartNet-Mobility, ACD, and ArtiCraft-10K demonstrate consistent improvements over both feed-forward and optimization-based baselines. Project page: https://kevinqu7.github.io/famos