Organization
Computer Vision and Learning research group at Ludwig Maximilian University of Munich (formerly Computer Vision Group at Heidelberg University)
62
Repository pubblici
100.472
Stelle totali
4404
Follower
CompVis è un'organizzazione su GitHub che si concentra sulla ricerca nel campo della visione artificiale e dell'apprendimento, con sede presso la Ludwig Maximilian University di Monaco. Utilizza diverse lingue di programmazione come Python, Jupyter Notebook e JavaScript, e ospita una vasta gamma di repository, tra cui progetti noti come stable-diffusion e latent-diffusion.
A latent text-to-image diffusion model
High-Resolution Image Synthesis with Latent Diffusion Models
Taming Transformers for High-Resolution Image Synthesis
[AAAI 2025, Oral] DepthFM: Fast Monocular Depth Estimation with Flow Matching
source code for the ECCV18 paper A Style-Aware Content Loss for Real-time HD Style Transfer
[CVPR 2026] A PyTorch implementation of the paper "EDGS: Eliminating Densification for Efficient Convergence of 3DGS"
A generative model conditioned on shape and appearance.
Is a geometric model required to synthesize novel views from a single image?
A PyTorch implementation of the paper "ZigMa: A DiT-Style Mamba-based Diffusion Model" (ECCV 2024)
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019
[ECCV 2024, Oral] FMBoost: Boosting Latent Diffusion with Flow Matching
Network-to-Network Translation with Conditional Invertible Neural Networks
Implementation of Stochastic Image-to-Video Synthesis using cINNs.
Nessuna descrizione fornita per questo repository.
TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".
Official codebase for the Paper “Retrieval-Augmented Diffusion Models”
Fine-Grained Subject-Specific Attribute Expression Control in T2I Models
Nessuna descrizione fornita per questo repository.
Nessuna descrizione fornita per questo repository.
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
The official implementation of "[MASK] is All You Need"
A Disentangling Invertible Interpretation Network
[CVPR 2025] Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment
Content and Style Disentanglement for Artistic Style Transfer [ICCV19]
[CVPR 2026] Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
Nessuna descrizione fornita per questo repository.
[ICLR 2026] Adapting Self-Supervised Representations as a Latent Space for Efficient Generation
Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with Invertible Neural Networks
Nessuna descrizione fornita per questo repository.
Nessuna descrizione fornita per questo repository.
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
[ICCV 2025] SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Nessuna descrizione fornita per questo repository.
Nessuna descrizione fornita per questo repository.
[WACV 2025] DistillDIFT: Distillation of Diffusion Features for Semantic Correspondence
Code for GCPR 2020 Oral : "Unsupervised Part Discovery by Unsupervised Disentanglement"
Nessuna descrizione fornita per questo repository.
[NeurIPS 2025] DisMo: DIsentangled Motion Representations for Open-World Motion Transfer
MaskFlow: Discrete Flows For Flexible and Efficient Long Video Generation
Nessuna descrizione fornita per questo repository.
[AAAI 2025] Does VLM Classification Benefit from LLM Description Semantics?
Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
Content Transformation Block For Image Style Transfer [CVPR19]
Source code for the paper "Improving Deep Metric Learning byDivide and Conquer"
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.
Visual search interface
Code for the article "Deep learning of cuneiform sign detection with weak supervision using transliteration alignment"
Towards Learning a Realistic Rendering of Human Behavior
Unsupervised Magnification of Posture Deviations Across Subjects
[CVPR 2026] Probabilistic Precipitation Nowcasting with Rectified Flow Transformers
[ICCV 2025] Stochastic Interpolants for Revealing Stylistic Flows across the History of Art
Landing point for "Envisioning the Future, One Step at a Time"
Nessuna descrizione fornita per questo repository.
Official project page for the paper "WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians"
Nessuna descrizione fornita per questo repository.
Source Code + Documentation of our Automatic Behavior Analysis Software
Code for demo web application of the article "Deep learning for cuneiform sign detection with weak supervision using transliteration alignment".
Nessuna descrizione fornita per questo repository.
Nessuna descrizione fornita per questo repository.
Code for our paper "CliqueCNN: Deep Unsupervised Exemplar Learning" https://arxiv.org/abs/1608.08792
Deep Unsupervised Similarity Learning using Partially Ordered Sets (CVPR17)
The official implementation of "[MASK] is All You Need"
CompVis sviluppa progetti legati alla visione artificiale e all'apprendimento automatico. I loro repository includono implementazioni di modelli di diffusione come stable-diffusion e latent-diffusion, che sono ampiamente utilizzati nella generazione di immagini.
CompVis utilizza principalmente Python e Jupyter Notebook, insieme ad altri linguaggi come JavaScript, HTML, Matlab e CSS. Questa varietà consente di affrontare diversi aspetti della ricerca e dello sviluppo nel campo della visione artificiale.
Sì, tutte le repository di CompVis sono pubbliche su GitHub. Questo consente a ricercatori e sviluppatori di accedere ai loro progetti e contribuire allo sviluppo della comunità nel campo della visione artificiale.
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