ContactArt: カテゴリーレベルの関節物体と手の姿勢推定のための3Dインタラクションプライアの学習
ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation
視覚的テレオペレーションで収集したデータセットと、関節物体と手の姿勢推定のための3Dインタラクションプライア（GANと拡散モデル）を学習する手法を提案し、実世界データへの転移で性能を大幅に改善した。
著者: Zehao Zhu, Jiashun Wang, Yuzhe Qin, Deqing Sun, Varun Jampani, Xiaolong Wang
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation. We first collect a dataset using visual teleoperation, where the human operator can directly play within a physical simulator to manipulate the articulated objects. We record the data and obtain free and accurate annotations on object poses and contact information from the simulator. Our system only requires an iPhone to record human hand motion, which can be easily scaled up and largely lower the costs of data and annotation collection. With this data, we learn 3D interaction priors including a discriminator (in a GAN) capturing the distribution of how object parts are arranged, and a diffusion model which generates the contact regions on articulated objects, guiding the hand pose estimation. Such structural and contact priors can easily transfer to real-world data with barely any domain gap. By using our data and learned priors, our method significantly improves the performance on joint hand and articulated object poses estimation over the existing state-of-the-art methods. The project is available at https://zehaozhu.github.io/ContactArt/ .
