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We develop a system for modeling hand-object interactions in 3D from RGBimages that show a hand which is holding a novel object from a known category.We design a Convolutional Neural Network (CNN) for Hand-held Object Pose andShape estimation called HOPS-Net and utilize prior work to estimate the handpose and configuration. We leverage the insight that information about the handfacilitates object pose and shape estimation by incorporating the hand intoboth training and inference of the object pose and shape as well as therefinement of the estimated pose. The network is trained on a large syntheticdataset of objects in interaction with a human hand. To bridge the gap betweenreal and synthetic images, we employ an image-to-image translation model(Augmented CycleGAN) that generates realistically textured objects given asynthetic rendering. This provides a scalable way of generating annotated datafor training HOPS-Net. Our quantitative experiments show that even noisy handparameters significantly help object pose and shape estimation. The qualitativeexperiments show results of pose and shape estimation of objects held by a hand"in the wild".

Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images