#Parametric 3d modeling in vr manual
In contrast, our NPMs can be learned from data belonging to a domain without requiring any expert knowledge or manual intervention.Īdditionally, approaches such as SMPL or GHUM are skinned vertex-based models which can struggle to represent complex surface features (e.g., wrinkles, clothing).īy leveraging recently proposed implicit functions, our approach can naturally capture more intricate surface detail. To construct such parametric models, various domain-specific annotations are often required, such as the number of parts or the kinematic chain. SMPL is a very popular parametric model of the human body based on blend shapes in combination with a skeleton, and constructed from a dataset of 3D body scans.Įxtensions exist to also model soft tissue and clothing. Parametric 3D models have become a predominant approach to disentangle 3D deformable shapes into several factors,, shape and pose, for domains such as human bodies, hands, animals and faces. Latent-space interpolation as well as shape/pose transfer experimentsįurther demonstrate the usefulness of NPMs. Reconstruction and tracking of monocular depth sequences of clothed humans and Improve notably over both parametric and non-parametric state of the art in This enables NPMs to achieve a significantly more accurateĪnd detailed representation of observed deformable sequences. To fit to new observations, similar to the fitting of a traditional parametric Parametric models of shape and pose enable optimization over the learned spaces Representations of shape and pose, leveraging the flexibility of recentĭevelopments in learned implicit functions. Particular, we learn to disentangle 4D dynamics into latent-space Models, which does not require hand-crafted, object-specific constraints. Models (NPMs), a novel, learned alternative to traditional, parametric 3D To this end, we propose Neural Parametric Heavy manual tweaking, and they struggle to represent additional complexity andĭetails such as wrinkles or clothing. The construction of these parametric models is often tedious, as it requires Graphics and vision, such as modeling human bodies, faces, and hands. Parametric 3D models have enabled a wide variety of tasks in computer NPMs: Neural Parametric Models for 3D Deformable Shapes