3 citations · 7 across the 8 of their papers we have counts for
8 papers
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
Tom Fischer, Yaoyao Liu, Artur Jesslen +6
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have rec…
Learning a Category-level Object Pose Estimator without Pose Annotations
Fengrui Tian, Yaoyao Liu, Adam Kortylewski +4
3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is lab…
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
Fengrui Tian, Yueqi Duan, Angtian Wang +2
In this work, we pioneer Semantic Flow, a neural semantic representation of dynamic scenes from monocular videos. In contrast to previous NeRF methods that reconstruct dynamic scen…
HISR: Hybrid Implicit Surface Representation for Photorealistic 3D Human Reconstruction
Angtian Wang, Yuanlu Xu, Nikolaos Sarafianos +4
Neural reconstruction and rendering strategies have demonstrated state-of-the-art performances due, in part, to their ability to preserve high level shape details. Existing approac…
3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation
Yi Zhang, Pengliang Ji, Angtian Wang +3
Regression-based methods for 3D human pose estimation directly predict the 3D pose parameters from a 2D image using deep networks. While achieving state-of-the-art performance on s…
Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis
Angtian Wang, Wufei Ma, Alan Yuille +1
Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios. It has been conjectured such robustness benefits from performing analysi…