8 papers
GNM Head: A Generative aNthropometric Model of the human head
Stylianos Ploumpis, Jan Bednarik, Gaspard Zoss +28
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 a…
VEPHand: View-Efficient Photometric Hand Performance Capture at Scale
Zhengyang Shen, Kai-Hung Chang, Erroll Wood +18
Robust, high-fidelity 3D hand capture, while fundamental to digital human creation, remains challenging with practical multi-view systems that balance rich photometry with the geom…
Topologically Consistent Multi-view 3D Head Reconstruction via Coarse-Guided Layered Surface Sampling
Timo Bolkart, Daoye Wang, Prashanth Chandran
We present SHELLS (Semantic Head Estimation via Layered Local Sampling), an efficient feed-forward framework for 3D head reconstruction in dense semantic correspondence from multi-…
Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
Panagiotis P. Filntisis, George Retsinas, Radek DanÄÄek +3
Recent frameworks like ToFu and TEMPEH provide an automated alternative to classical registration pipelines by predicting 3D meshes in dense semantic correspondence directly from c…
3DRealHead: Few-Shot Detailed Head Avatar
Jalees Nehvi, Timo Bolkart, Thabo Beeler +1
The human face is central to communication. For immersive applications, the digital presence of a person should mirror the physical reality, capturing the users idiosyncrasies and…
Representing 3D Faces with Learnable B-Spline Volumes
Prashanth Chandran, Daoye Wang, Timo Bolkart
We present CUBE (Control-based Unified B-spline Encoding), a new geometric representation for human faces that combines B-spline volumes with learned features, and demonstrate its…