PAWS：从全景视频中大规模感知动作
PAWS: Perception of Articulation in the Wild at Scale from Egocentric Videos
AI总结：
PAWS通过直接提取手-物体互动中的动作结构，提升机器人、仿真和动画中的3D场景理解能力，实现大规模野外视频中的动作感知突破。
摘要：
动作感知旨在恢复动作和结构（如抽屉和柜子），是机器人、仿真和动画中3D场景理解的基础。现有基于学习的方法依赖高质量3D数据和手动标注进行监督训练，限制了可扩展性和多样性。为解决这一限制，我们提出PAWS，一种直接从大规模野外全景视频中手-物体互动中提取物体动作的方法。我们在公开数据集HD-EPIC和Arti4D上评估了该方法，显著优于基线。我们进一步证明，提取的动作有助于下游任务，包括微调3D动作预测模型和使机器人操作。请查看项目网站：https://aaltoml.github.io/PAWS/.
英文摘要：
Articulation perception aims to recover the motion and structure of articulated objects (e.g., drawers and cupboards), and is fundamental to 3D scene understanding in robotics, simulation, and animation. Existing learning-based methods rely heavily on supervised training with high-quality 3D data and manual annotations, limiting scalability and diversity. To address this limitation, we propose PAWS, a method that directly extracts object articulations from hand-object interactions in large-scale in-the-wild egocentric videos. We evaluate our method on the public data sets, including HD-EPIC and Arti4D data sets, achieving significant improvements over baselines. We further demonstrate that the extracted articulations benefit downstream tasks, including fine-tuning 3D articulation prediction models and enabling robot manipulation. See the project website at https://aaltoml.github.io/PAWS/.
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。
