no code implementations • 23 Sep 2021 • Korrawe Karunratanakul, Adrian Spurr, Zicong Fan, Otmar Hilliges, Siyu Tang
We present Hand ArticuLated Occupancy (HALO), a novel representation of articulated hands that bridges the advantages of 3D keypoints and neural implicit surfaces and can be used in end-to-end trainable architectures.
1 code implementation • 1 Jul 2021 • Zicong Fan, Adrian Spurr, Muhammed Kocabas, Siyu Tang, Michael J. Black, Otmar Hilliges
In natural conversation and interaction, our hands often overlap or are in contact with each other.
Ranked #7 on 3D Interacting Hand Pose Estimation on InterHand2.6M
no code implementations • 10 Jun 2021 • Adrian Spurr, Pavlo Molchanov, Umar Iqbal, Jan Kautz, Otmar Hilliges
Hand pose estimation is difficult due to different environmental conditions, object- and self-occlusion as well as diversity in hand shape and appearance.
1 code implementation • ICCV 2021 • Adrian Spurr, Aneesh Dahiya, Xi Wang, Xucong Zhang, Otmar Hilliges
Encouraged by the success of contrastive learning on image classification tasks, we propose a new self-supervised method for the structured regression task of 3D hand pose estimation.
no code implementations • ECCV 2020 • Anil Armagan, Guillermo Garcia-Hernando, Seungryul Baek, Shreyas Hampali, Mahdi Rad, Zhaohui Zhang, Shipeng Xie, Mingxiu Chen, Boshen Zhang, Fu Xiong, Yang Xiao, Zhiguo Cao, Junsong Yuan, Pengfei Ren, Weiting Huang, Haifeng Sun, Marek Hrúz, Jakub Kanis, Zdeněk Krňoul, Qingfu Wan, Shile Li, Linlin Yang, Dongheui Lee, Angela Yao, Weiguo Zhou, Sijia Mei, Yun-hui Liu, Adrian Spurr, Umar Iqbal, Pavlo Molchanov, Philippe Weinzaepfel, Romain Brégier, Grégory Rogez, Vincent Lepetit, Tae-Kyun Kim
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
no code implementations • ECCV 2020 • Adrian Spurr, Umar Iqbal, Pavlo Molchanov, Otmar Hilliges, Jan Kautz
Estimating 3D hand pose from 2D images is a difficult, inverse problem due to the inherent scale and depth ambiguities.
Ranked #10 on 3D Hand Pose Estimation on DexYCB
1 code implementation • ICCV 2019 • Zhe He, Adrian Spurr, Xucong Zhang, Otmar Hilliges
In this work, we present a novel method to alleviate this problem by leveraging generative adversarial training to synthesize an eye image conditioned on a target gaze direction.
1 code implementation • ECCV 2018 • Seonwook Park, Adrian Spurr, Otmar Hilliges
In this paper, we introduce a novel deep neural network architecture specifically designed for the task of gaze estimation from single eye input.
1 code implementation • CVPR 2018 • Adrian Spurr, Jie Song, Seonwook Park, Otmar Hilliges
Furthermore, we show that our proposed method can be used without changes on depth images and performs comparably to specialized methods.
2 code implementations • 14 Jul 2017 • Adrian Spurr, Emre Aksan, Otmar Hilliges
In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0. 22%, max.