9 citations · 17 across the 3 of their papers we have counts for
6 papers · 1 filter
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
Thiemo Alldieck, Mihai Zanfir, Cristian Sminchisescu
We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image. Our pixel-aligned me…
HSPACE: Synthetic Parametric Humans Animated in Complex Environments
Eduard Gabriel Bazavan, Andrei Zanfir, Mihai Zanfir +3
Advances in the state of the art for 3d human sensing are currently limited by the lack of visual datasets with 3d ground truth, including multiple people, in motion, operating in…
THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers
Mihai Zanfir, Andrei Zanfir, Eduard Gabriel Bazavan +3
We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images. Key to our methodology is an inte…
Learning Complex 3D Human Self-Contact
Mihai Fieraru, Mihai Zanfir, Elisabeta Oneata +3
Monocular estimation of three dimensional human self-contact is fundamental for detailed scene analysis including body language understanding and behaviour modeling. Existing 3d re…
Human Synthesis and Scene Compositing
Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa +2
Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important…
Deep Multitask Architecture for Integrated 2D and 3D Human Sensing
Alin-Ionut Popa, Mihai Zanfir, Cristian Sminchisescu
We propose a deep multitask architecture for \emph{fully automatic 2d and 3d human sensing} (DMHS), including \emph{recognition and reconstruction}, in \emph{monocular images}. The…