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Computer Vision Group, Albert-Ludwigs-Universität Freiburg's Projects

contra-hand icon contra-hand

Code in conjunction with the publication 'Contrastive Representation Learning for Hand Shape Estimation'

deeptam icon deeptam

DeepTAM: Deep Tracking and Mapping https://lmb.informatik.uni-freiburg.de/people/zhouh/deeptam/

demon icon demon

DeMoN: Depth and Motion Network

fln-epn-rpn icon fln-epn-rpn

This repository contains the source code of the CVPR 2020 paper: "Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior"

flownet2 icon flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

flownet2-docker icon flownet2-docker

Dockerfile and runscripts for FlowNet 2.0 (estimation of optical flow)

freihand icon freihand

A dataset for estimation of hand pose and shape from single color images.

freipose-docker icon freipose-docker

FreiPose: A Deep Learning Framework for Precise Animal Motion Capture in 3D Spaces

hand3d icon hand3d

Network estimating 3D Handpose from single color images

ldce icon ldce

Official repository for "Latent Diffusion Counterfactual Explanations"

locov icon locov

Localized Vision-Language Matching for Open-vocabulary Object Detection

mbeval icon mbeval

evaluation code for motion boundaries

multimodal-future-prediction icon multimodal-future-prediction

The official repository for the CVPR 2019 paper "Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction"

mv3d icon mv3d

Multi-view 3D Models from Single Images with a Convolutional Network

netdef_models icon netdef_models

Repository for different network models related to flow/disparity (ECCV 18)

netdef_slim icon netdef_slim

A python wrapper for tf to ease creation of network definitions.

ogn icon ogn

Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs

optical-flow-2d-data-generation icon optical-flow-2d-data-generation

Caffe(v1)-compatible codebase to generate optical flow training data on-the-fly; used for the IJCV 2018 paper "What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?" (http://dx.doi.org/10.1007/s11263-018-1082-6)

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