Marc Stamminger
Computer Vision and Pattern Recognition · 2013–2019
A researcher in the Science corpus: 8 papers; trusts 279 researchers and is trusted by 357 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 184 citations into it; #2,186 of 21,575 by trust here, #1,641 by citations
- Computer Graphics and Computer-Aided Design — 43 citations into it; #346 of 1,584 by trust here, #251 by citations
- Computational Mechanics — 40 citations into it
- Aerospace Engineering — 33 citations into it; #782 of 6,421 by trust here, #531 by citations
- Control and Systems Engineering — 24 citations into it
- Geology — 22 citations into it
- 3D Vision & Pose — 15 citations into it; #236 of 689 by trust here, #220 by citations
- Signal Processing — 12 citations into it
Whom Marc Stamminger trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David G. Lowe
- Diederik P. Kingma
- Shahram Izadi
- Michael Zollhöfer
- Davide Cozzolino
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Hao Li
- Yoshua Bengio
Who trusts Marc Stamminger overall
- Justus Thies
- Michael Zollhöfer
- Matthias Nießner
- Angela Dai
- Ayush Kumar Tewari
- Hyeongwoo Kim
- Yuezun Li
- Thanh Thi Nguyen
- Aliaksandr Siarohin
- Luisa Verdoliva
Papers from Marc Stamminger's perspective
Ordered by how strongly the researchers Marc Stamminger trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Face2Face: Real-Time Face Capture and Reenactment of RGB Videos (2016) — 2,282 citations
- Real-time 3D reconstruction at scale using voxel hashing (2013) — 1,099 citations
- Real-time non-rigid reconstruction using an RGB-D camera (2014) — 467 citations
- Real-time expression transfer for facial reenactment (2015) — 404 citations
- VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction (2016) — 349 citations
- State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications (2018) — 344 citations
- Shading-based refinement on volumetric signed distance functions (2015) — 136 citations
- Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations (2019) — 751 citations