6 papers · 1 filter
Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining
Junxuan Li, Rawal Khirodkar, Chengan He +37
High-quality 3D avatar modeling faces a critical trade-off between fidelity and generalization. On the one hand, multi-view studio data enables high-fidelity modeling of humans wit…
Realistic Clothed Human and Object Joint Reconstruction from a Single Image
Ayushi Dutta, Marco Pesavento, Marco Volino +2
Recent approaches to jointly reconstruct 3D humans and objects from a single RGB image represent 3D shapes with template-based or coarse models, which fail to capture details of lo…
COSMU: Complete 3D human shape from monocular unconstrained images
Marco Pesavento, Marco Volino, Adrian Hilton
We present a novel framework to reconstruct complete 3D human shapes from a given target image by leveraging monocular unconstrained images. The objective of this work is to reprod…
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image
Marco Pesavento, Yuanlu Xu, Nikolaos Sarafianos +7
Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB im…
Super-Resolution Appearance Transfer for 4D Human Performances
Marco Pesavento, Marco Volino, Adrian Hilton
A common problem in the 4D reconstruction of people from multi-view video is the quality of the captured dynamic texture appearance which depends on both the camera resolution and…
Attention-based Multi-Reference Learning for Image Super-Resolution
Marco Pesavento, Marco Volino, Adrian Hilton
This paper proposes a novel Attention-based Multi-Reference Super-resolution network (AMRSR) that, given a low-resolution image, learns to adaptively transfer the most similar text…