The paper introduces GNM Head, a generative anthropometric model that extends beyond traditional head models by including detailed ocular and intra‑oral structures such as eyeballs, teeth, and tongue. Built from a large collection of high‑resolution 3D scans and artist‑crafted samples, GNM offers improved geometric fidelity and state‑of‑the‑art fitting performance for target 3D face scans. The authors release the full framework publicly to support further research and development.
Parametric models of the human head are essential tools traditionally used in computer vision and graphics for animation, rendering, and reconstruction. More recently, they serve as crucial conditioning signals within generative large vision models, allowing for tight spatial control of generated imagery.
arXiv:2608.22655v1 Announce Type: new Abstract: Creating re-topologized 3D facial meshes is essential for high-quality facial animation but remains labor-intensive and time-consuming. This dissertati...
arXiv:2407. 18245v3 Announce Type: replace-cross Abstract: Human head detection, keypoint estimation, and 3D head model fitting are essential tasks with many applications.
arXiv:2607. 05585v1 Announce Type: cross Abstract: FaceMesh2HPO is a framework for classifying facial phenotypic descriptors aligned with the Human Phenotype Ontology (HPO) to support clinical diagnosis.
3D Morphable Models (3DMMs) remain the standard parametric shape priors for many state-of-the-art 3D face reconstruction algorithms. However, as these models are derived from a finite number of 3D face samples, they inherit the morphological biases of their training data, potentially limiting their generalizability across diverse global populations.
arXiv:2609.12850v1 Announce Type: new Abstract: Accurate head modeling requires a stable yet expressive geometric representation. Existing Gaussian-based head avatars commonly rely on parametric temp...
arXiv:2601. 02088v3 Announce Type: replace Abstract: Orthognathic surgery repositions jaw bones to restore occlusion and enhance facial aesthetics.
arXiv:2608.22131v1 Announce Type: cross Abstract: Craniosynostosis severity analysis increasingly relies on statistical shape models (SSMs) to quantify cranial morphology, but most existing workflows...
arXiv:2605. 04035v3 Announce Type: replace-cross Abstract: We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups.
arXiv:2606. 05998v1 Announce Type: cross Abstract: Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are commonly used for this phase, each with notable limitations.
The paper introduces AvaImg, a multi‑stage optimization pipeline that achieves high‑fidelity SMPL(-X)+D registrations with UV texture for arbitrary clothed scans. By enforcing a body‑inside‑clothing constraint through signed winding numbers and employing a three‑level efficiency cascade, AvaImg significantly reduces runtime and storage while recovering fine surface detail via coarse‑to‑fine displacement optimization. The resulting textured registrations are nearly indistinguishable from scans, and encoding the UV maps with a frozen FLUX VAE demonstrates compatibility with 2D generative models, enabling 3D avatar generation using image‑based priors.
arXiv:2608.23410v1 Announce Type: new Abstract: Photorealistic novel view synthesis of people remains challenging at high spatial resolutions and across multiple target cameras, where preserving iden...