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- Max Planck SocietyDE261 papers
- Saarland UniversityDE124 papers
- Technische Universität BerlinDE55 papers
- Centre National de la Recherche ScientifiqueFR41 papers
- Korea UniversityKR41 papers
- Stanford UniversityUS25 papers
- Institut national de recherche en sciences et technologies du numériqueFR24 papers
- ETH ZurichCH21 papers
- Helmholtz Center for Information SecurityDE20 papers
- Technical University of MunichDE20 papers
- Université de LorraineFR17 papers
- University of LuxembourgLU16 papers
25 papers · 2 filters
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik +3
We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a d…
i3DMM: Deep Implicit 3D Morphable Model of Human Heads
Tarun Yenamandra, Ayush Tewari, Florian Bernard +4
We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expr…
PoseTrackReID: Dataset Description
Andreas Doering, Di Chen, Shanshan Zhang +2
Current datasets for video-based person re-identification (re-ID) do not include structural knowledge in form of human pose annotations for the persons of interest. Nonetheless, po…
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs
Hui-Po Wang, Ning Yu, Mario Fritz
While Generative Adversarial Networks (GANs) show increasing performance and the level of realism is becoming indistinguishable from natural images, this also comes with high deman…
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Alexandre Saint, Anis Kacem, Kseniya Cherenkova +7
The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D…
Adaptive Aggregation Networks for Class-Incremental Learning
Yaoyao Liu, Bernt Schiele, Qianru Sun
Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity d…