Name: CompVis - Computer Vision and Learning LMU Munich
Type: Organization
Bio: Computer Vision and Learning research group at Ludwig Maximilian University of Munich (formerly Computer Vision Group at Heidelberg University)
Location: Germany
Blog: https://ommer-lab.com/
CompVis - Computer Vision and Learning LMU Munich's Projects
source code for the ECCV18 paper A Style-Aware Content Loss for Real-time HD Style Transfer
Fine-Grained Subject-Specific Attribute Expression Control in T2I Models
Source Code + Documentation of our Automatic Behavior Analysis Software
TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".
Code for our paper "CliqueCNN: Deep Unsupervised Exemplar Learning" https://arxiv.org/abs/1608.08792
Content and Style Disentanglement for Artistic Style Transfer [ICCV19]
Content Transformation Block For Image Style Transfer [CVPR19]
Code for the article "Deep learning of cuneiform sign detection with weak supervision using transliteration alignment"
Dataset provided with the article "Deep learning for cuneiform sign detection with weak supervision using transliteration alignment". It comprises image references, transliterations and sign annotations of clay tablets from the Neo-Assyrian epoch.
Code for demo web application of the article "Deep learning for cuneiform sign detection with weak supervision using transliteration alignment".
Deep Unsupervised Similarity Learning using Partially Ordered Sets (CVPR17)
DepthFM: Fast Monocular Depth Estimation with Flow Matching
FMBoost: Boosting Latent Diffusion with Flow Matching (ECCV 2024 Oral)
Is a geometric model required to synthesize novel views from a single image?
Towards Learning a Realistic Rendering of Human Behavior
A Disentangling Invertible Interpretation Network
Implementation of Stochastic Image-to-Video Synthesis using cINNs.
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with Invertible Neural Networks
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
High-Resolution Image Synthesis with Latent Diffusion Models
Unsupervised Magnification of Posture Deviations Across Subjects
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019
Source code for the paper "Improving Deep Metric Learning byDivide and Conquer"
Network-to-Network Translation with Conditional Invertible Neural Networks