DailyArt: 从单张静态图像中通过潜在动态发现关节
DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics
AI总结：
DailyArt通过合成介导推理解决单张图像中关节估计问题，无需多视角输入或显式部分标注，实现关节参数同时恢复和部分级新状态合成。
摘要：
关节化物体对具身AI和世界模型至关重要，但从单张闭合状态图像推断其运动学仍具挑战性，因为关键运动线索常被遮挡。现有方法要么需要多状态观测，要么依赖显式部分先验、检索或其他辅助输入。本文提出DailyArt，将关节估计从单张图像视为合成介导推理问题。而非直接从高度遮挡的观测中回归关节，DailyArt首先合成一个最大关节展开状态以暴露关节线索，然后从观测与合成状态的差异估计全部关节参数。使用集预测公式，DailyArt在测试时无需物体特定模板、多视角输入或显式部分标注即可恢复所有关节。将估计的关节作为条件，框架进一步支持部分级新状态合成。大量实验表明，DailyArt在关节估计中表现优异，并支持基于关节的部分级新状态合成。项目页面可在https://rangooo123.github.io/DaliyArt.github.io/访问。
英文摘要：
Articulated objects are essential for embodied AI and world models, yet inferring their kinematics from a single closed-state image remains challenging because crucial motion cues are often occluded. Existing methods either require multi-state observations or rely on explicit part priors, retrieval, or other auxiliary inputs that partially expose the structure to be inferred. In this work, we present DailyArt, which formulates articulated joint estimation from a single static image as a synthesis-mediated reasoning problem. Instead of directly regressing joints from a heavily occluded observation, DailyArt first synthesizes a maximally articulated opened state under the same camera view to expose articulation cues, and then estimates the full set of joint parameters from the discrepancy between the observed and synthesized states. Using a set-prediction formulation, DailyArt recovers all joints simultaneously without requiring object-specific templates, multi-view inputs, or explicit part annotations at test time. Taking estimated joints as conditions, the framework further supports part-level novel state synthesis as a downstream capability. Extensive experiments show that DailyArt achieves strong performance in articulated joint estimation and supports part-level novel state synthesis conditioned on joints. Project page is available at https://rangooo123.github.io/DaliyArt.github.io/.
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。
