most cited3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023

3D-Aware Visual Question Answering about Parts, Poses and Occlusions

Xingrui Wang, Wufei Ma, Zhuowan Li +2

Despite rapid progress in Visual question answering (VQA), existing datasets and models mainly focus on testing reasoning in 2D. However, it is important that VQA models also under…

cs.CV20232 cited

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…

cs.CV2023

AvatarStudio: Text-driven Editing of 3D Dynamic Human Head Avatars

Mohit Mendiratta, Xingang Pan, Mohamed Elgharib +6

Capturing and editing full head performances enables the creation of virtual characters with various applications such as extended reality and media production. The past few years…

cs.CV20231 cited

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…

cs.CV2023

Robust Category-Level 3D Pose Estimation from Synthetic Data

Jiahao Yang, Wufei Ma, Angtian Wang +3

Obtaining accurate 3D object poses is vital for numerous computer vision applications, such as 3D reconstruction and scene understanding. However, annotating real-world objects is…

cs.CV2023

PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation

Qihao Liu, Adam Kortylewski, Alan Yuille

Human pose and shape (HPS) estimation methods achieve remarkable results. However, current HPS benchmarks are mostly designed to test models in scenarios that are similar to the tr…