Dynamic Multiview Refinement of 3D Hand Datasets using Differentiable Ray Tracing

With the increasing importance of AI applications in the field of 3D estimation of hand state, the quality of the datasets used for training the relevant models is of utmost importance. Especially in the case of datasets consisting of real-world images, the quality of annotations, i.e., how accurately the provided ground truth reflects the true state of the scene, can greatly affect the performance of downstream applications. In this work, we propose a methodology with significant impact on improving ubiquitous 3D hand geometry datasets that contain real images with imperfect annotations. Our approach leverages multi-view imagery, temporal consistency, and a disentangled representation of hand shape, texture, and environment lighting. This allows to refine the hand geometry of existing datasets and also paves the way for texture extraction. Extensive experiments on synthetic and real-world data show that our method outperforms the current state of the art, resulting in more accurate and visually pleasing reconstructions of hand gestures.