58 citations · 117 across the 14 of their papers we have counts for
17 papers
Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement
Jian Wang, Zhe Cao, Diogo Luvizon +5
In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion. This task presents significa…
Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis
Marcel C. Bühler, Kripasindhu Sarkar, Tanmay Shah +8
NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin. These methods typically require a large number o…
Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
Ziqian Bai, Feitong Tan, Zeng Huang +12
We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achie…
Scene-aware Egocentric 3D Human Pose Estimation
Jian Wang, Lingjie Liu, Weipeng Xu +3
Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Ex…
Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision
Jian Wang, Lingjie Liu, Weipeng Xu +3
Egocentric 3D human pose estimation with a single fisheye camera has drawn a significant amount of attention recently. However, existing methods struggle with pose estimation from…
EgoRenderer: Rendering Human Avatars from Egocentric Camera Images
Tao Hu, Kripasindhu Sarkar, Lingjie Liu +2
We present EgoRenderer, a system for rendering full-body neural avatars of a person captured by a wearable, egocentric fisheye camera that is mounted on a cap or a VR headset. Our…
Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control
Lingjie Liu, Marc Habermann, Viktor Rudnev +3
We propose Neural Actor (NA), a new method for high-quality synthesis of humans from arbitrary viewpoints and under arbitrary controllable poses. Our method is built upon recent ne…
Estimating Egocentric 3D Human Pose in Global Space
Jian Wang, Lingjie Liu, Weipeng Xu +2
Egocentric 3D human pose estimation using a single fisheye camera has become popular recently as it allows capturing a wide range of daily activities in unconstrained environments,…
HumanGAN: A Generative Model of Humans Images
Kripasindhu Sarkar, Lingjie Liu, Vladislav Golyanik +1
Generative adversarial networks achieve great performance in photorealistic image synthesis in various domains, including human images. However, they usually employ latent vectors…
Style and Pose Control for Image Synthesis of Humans from a Single Monocular View
Kripasindhu Sarkar, Vladislav Golyanik, Lingjie Liu +1
Photo-realistic re-rendering of a human from a single image with explicit control over body pose, shape and appearance enables a wide range of applications, such as human appearanc…
Neural Re-Rendering of Humans from a Single Image
Kripasindhu Sarkar, Dushyant Mehta, Weipeng Xu +2
Human re-rendering from a single image is a starkly under-constrained problem, and state-of-the-art algorithms often exhibit undesired artefacts, such as over-smoothing, unrealisti…
Pose-Guided Human Animation from a Single Image in the Wild
Jae Shin Yoon, Lingjie Liu, Vladislav Golyanik +3
We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses. Existing pose transfer methods exhi…
Object Detection and Recognition of Swap-Bodies using Camera mounted on a Vehicle
Ebin Zacharias, Didier Stricker, Martin Teuchler +1
Object detection and identification is a challenging area of computer vision and a fundamental requirement for autonomous cars. This project aims to jointly perform object detectio…
Learning Quadrangulated Patches For 3D Shape Processing
Kripasindhu Sarkar, Kiran Varanasi, Didier Stricker
We propose a system for surface completion and inpainting of 3D shapes using generative models, learnt on local patches. Our method uses a novel encoding of height map based local…
Structured 2D Representation of 3D Data for Shape Processing
Kripasindhu Sarkar, Elizabeth Mathews, Didier Stricker
We represent 3D shape by structured 2D representations of fixed length making it feasible to apply well investigated 2D convolutional neural networks (CNN) for both discriminative…
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
Kripasindhu Sarkar, Basavaraj Hampiholi, Kiran Varanasi +1
We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs. We represent 3D shapes as multi-layered height-maps (MLH) where at each grid locatio…
Learning quadrangulated patches for 3D shape parameterization and completion
Kripasindhu Sarkar, Kiran Varanasi, Didier Stricker
We propose a novel 3D shape parameterization by surface patches, that are oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail on local patches, we learn…