ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation
This work addresses the challenge of accurately estimating poses in hand-object interactions for robotics and AR/VR applications, representing a novel method for a known bottleneck.
The authors tackled the problem of joint hand and articulated object pose estimation by introducing a new dataset collected via visual teleoperation and learning 3D interaction priors, resulting in significant performance improvements over state-of-the-art methods.
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/ .