no code implementations • 28 Nov 2023 • Jian Wang, Zhe Cao, Diogo Luvizon, Lingjie Liu, Kripasindhu Sarkar, Danhang Tang, Thabo Beeler, Christian Theobalt
In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion.
Ranked #1 on Egocentric Pose Estimation on GlobalEgoMocap Test Dataset (using extra training data)
no code implementations • ICCV 2023 • Marcel C. Bühler, Kripasindhu Sarkar, Tanmay Shah, Gengyan Li, Daoye Wang, Leonhard Helminger, Sergio Orts-Escolano, Dmitry Lagun, Otmar Hilliges, Thabo Beeler, Abhimitra Meka
NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin.
no code implementations • CVPR 2023 • Ziqian Bai, Feitong Tan, Zeng Huang, Kripasindhu Sarkar, Danhang Tang, Di Qiu, Abhimitra Meka, Ruofei Du, Mingsong Dou, Sergio Orts-Escolano, Rohit Pandey, Ping Tan, Thabo Beeler, Sean Fanello, yinda zhang
The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses.
1 code implementation • CVPR 2023 • Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Diogo Luvizon, Christian Theobalt
To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network.
Ranked #3 on Egocentric Pose Estimation on GlobalEgoMocap Test Dataset (using extra training data)
no code implementations • CVPR 2022 • Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Diogo Luvizon, Christian Theobalt
Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision.
Ranked #4 on Egocentric Pose Estimation on GlobalEgoMocap Test Dataset (using extra training data)
no code implementations • ICCV 2021 • Tao Hu, Kripasindhu Sarkar, Lingjie Liu, Matthias Zwicker, Christian Theobalt
We next combine the target pose image and the textures into a combined feature image, which is transformed into the output color image using a neural image translation network.
no code implementations • 3 Jun 2021 • Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, Christian Theobalt
To address this problem, we utilize a coarse body model as the proxy to unwarp the surrounding 3D space into a canonical pose.
1 code implementation • ICCV 2021 • Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Christian Theobalt
Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective.
Ranked #4 on Egocentric Pose Estimation on SceneEgo (using extra training data)
no code implementations • 11 Mar 2021 • Kripasindhu Sarkar, Lingjie Liu, Vladislav Golyanik, Christian Theobalt
We address these limitations and present a generative model for images of dressed humans offering control over pose, local body part appearance and garment style.
no code implementations • 22 Feb 2021 • Kripasindhu Sarkar, Vladislav Golyanik, Lingjie Liu, Christian Theobalt
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 appearance transfer, virtual try-on, motion imitation, and novel view synthesis.
no code implementations • ECCV 2020 • Kripasindhu Sarkar, Dushyant Mehta, Weipeng Xu, Vladislav Golyanik, Christian Theobalt
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, unrealistic distortions of the body parts and garments, or implausible changes of the texture.
no code implementations • CVPR 2021 • Jae Shin Yoon, Lingjie Liu, Vladislav Golyanik, Kripasindhu Sarkar, Hyun Soo Park, Christian Theobalt
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.
no code implementations • 17 Apr 2020 • Ebin Zacharias, Didier Stricker, Martin Teuchler, Kripasindhu Sarkar
Collecting enough images for training the model is a critical step towards achieving good results.
no code implementations • 25 Mar 2019 • 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.
no code implementations • 25 Mar 2019 • 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 and geometric tasks on 3D shapes.
1 code implementation • ECCV 2018 • Kripasindhu Sarkar, Basavaraj Hampiholi, Kiran varanasi, Didier Stricker
We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs.
no code implementations • 20 Sep 2017 • Kripasindhu Sarkar, Kiran varanasi, Didier Stricker
By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the shape.