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favicon.ico: jankautz.com - Jan Kautz.

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site title: Jan Kautz

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oral propagation network s liu j gu s de mello v jampani g zhong m h yang j kautz european conference on computer vision eccv september 2018 pdf todo eccv tackling 3d tof artifacts through learning and the flat dataset q guo i frosio o gallo t zickler j kautz european conference on computer vision eccv september 2018 pdf todo eccv seal a framework towards simultaneous edge alignment and learning z yu w liu y zou c feng s ramalingam b v k v kumar j kautz european conference on computer vision eccv september 2018 pdf supplemental todo eccv superpixel sampling networks v jampani d sun m y liu m h yang j kautz european conference on computer vision eccv september 2018 pdf supplemental todo eccv a closed form solution to photorealistic image stylization y li m y liu x li m h yang j kautz european conference on computer vision eccv september 2018 pdf code todo eccv learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation z lv k kim a troccoli d sun j rehg j kautz european conference on computer vision eccv september 2018 pdf supplemental todo eccv hgmr hierarchical gaussian mixtures for adaptive 3d registration b eckart k kim j kautz european conference on computer vision eccv september 2018 pdf todo bmvc eoe expected overlap estimation over unstructured point cloud data b eckart k kim j kautz international conference on 3d vision 3dv september 2018 pdf todo bmvc budget aware activity detection with a recurrent policy network b mahesseni x yang p molchanov j kautz british machine vision conference bmvc september 2018 pdf supplemental todo cvpr pwc net cnns for optical flow using pyramid warping and cost volume d sun x yang m y liu j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 oral pdf supplemental code todo cvpr splatnet sparse lattice networks for point cloud processing h su v jampani d sun s maji e kalogerakis m h yang j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 best paper honorable mention award oral pdf supplemental todo cvpr high resolution image synthesis and semantic manipulation with conditional gans t c wang m y liu j y zhu a tao j kautz b catanzaro ieee conference on computer vision and pattern recognition cvpr june 2018 oral pdf webpage code todo cvpr geometry aware learning of maps for camera localization s brahmbhatt j gu k kim j hays j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 spotlight pdf supplemental code todo cvpr super slomo high quality estimation of multiple intermediate frames for video interpolation h jiang d sun v jampani m h yang e learned miller j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 spotlight pdf supplemental todo cvpr depth based 3d hand pose estimation from current achievements to future goals s yuan g garcia hernando b stenger g moon j y chang k m lee p molchanov j kautz s honari l ge j yuan x chen g wang f yang k akiyama y wu q wan m madadi s escalera s li d lee i oikonomidis a argyros t k kim ieee conference on computer vision and pattern recognition cvpr june 2018 spotlight pdf todo cvpr learning superpixels with segmentation aware affinity loss w c tu m y liu v jampani d sun s y chien m h yang j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 pdf supplemental todo cvpr deep semantic face deblurring z shen w s lai t xu j kautz m h yang ieee conference on computer vision and pattern recognition cvpr june 2018 pdf supplemental todo cvpr mocogan decomposing motion and content for video generation s tulyakov m y liu x yang j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 pdf todo cvpr improving landmark localization with semi supervised learning s honari p molchanov s tyree c pal p vincent j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 pdf supplemental todo cvpr making convolutional networks recurrent for visual sequence learning x yang p molchanov j kautz ieee conference on computer vision and pattern recognition cvpr june 2018 pdf supplemental todo iccp reblur2deblur deblurring videos via self supervised learning h chen j gu o gallo m y liu a veeraraghavan j kautz international conference on computational photography iccp may 2018 arxiv supplemental todo icra synthetically trained networks for learning human readable plans from real world demonstrations j tremblay t to a molchanov s tyree j kautz s birchfield international conference on robotics and automation icra may 2018 pdf todo aaai learning binary residual representations for domain specific video streaming y h tsai m y liu d sun m h yang j kautz aaai conference on artificial intelligence aaai february 2018 spotlight pdf video blog todo ei learning adaptive parameter tuning for image processing j dong i frosio j kautz electronic imaging image processing algorithms and systems xvi january 2018 pdf todo 2017 nips unsupervised image to image translation m y liu t breuel j kautz neural information processing systems nips december 2017 spotlight pdf supplemental todo nips learning affinity via spatial propagation networks s liu s de mello j gu g zhong m s yang j kautz neural information processing systems nips december 2017 pdf supplemental todo iccv intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting r maier k kim d cremers j kautz m niessner ieee international conference on computer vision iccv october 2017 pages 3133 3141 pdf supplemental more todo iccv a lightweight approach for on the fly reflectance estimation k kim j gu s tyree p molchanov m niessner j kautz ieee international conference on computer vision iccv october 2017 pages 20 28 oral pdf supplemental more todo 3dv cascaded scene flow prediction using semantic segmentation z ren d sun j kautz e sudderth international conference on 3d vision october 2017 oral pdf supplemental arxiv todo 3dv multiframe scene flow with piecewise rigid motion v golyanik k kim r maier m niessner d stricker j kautz international conference on 3d vision october 2017 spotlight pdf arxiv todo siggraph computational zoom a framework for post capture image composition a badki o gallo j kautz p sen acm transactions on graphics proceedings siggraph 2017 36 4 july 2017 pages 46 1 46 14 pdf webpage more todo siggraph mixed primary factorization for dual frame computational displays f c huang d pajak j kim j kautz d luebke acm transactions on graphics proceedings siggraph 2017 36 4 july 2017 pages 149 1 149 13 pdf more todo cvprw reconstructing intensity images from binary spatial gradient cameras s jayasuriya o gallo j gu t aila j kautz ieee cvpr embedded vision workshop july 2017 pdf more binary gradient cameras extract edge and temporal information directly on the sensor allowing for low power low bandwidth and high dynamic range capabilities all critical factors for the deployment of embedded computer vision systems however these types of images require specialized computer vision algorithms and are not easy to interpret by a human observer in this paper we propose to recover an intensity image from a single binary spatial gradient image with a deep autoencoder extensive experimental results on both simulated and real data show the effectiveness of the proposed approach cvpr dynamic facial analysis from bayesian filtering to recurrent neural network j gu s de mello x yang j kautz ieee conference on computer vision and pattern recognition cvpr july 2017 pages 1531 1540 pdf supplemental more facial analysis in videos including head pose estimation and facial landmark localization is key for many applications such as facial animation capture human activity recognition and human computer interaction in this paper we propose to use a recurrent neural network rnn for joint estimation and tracking of facial features in videos we are inspired by the fact that the computation performed in an rnn bears resemblance to bayesian filters which have been used for tracking in many previous methods for facial analysis from videos bayesian filters used in these methods however require complicated problem specific design and tuning in contrast our proposed rnn based method avoids such tracker engineering by learning from training data similar to how a convolutional neural network cnn avoids feature engineering for image classification as an end to end network the proposed rnn based method provides a generic and holistic solution for joint estimation and tracking of various types of facial features from consecutive video frames extensive experimental results on head pose estimation and facial landmark localization from videos demonstrate that the proposed rnn based method outperforms frame wise models and bayesian filtering in addition we create a large scale synthetic dataset for head pose estimation with which we achieve state of the art performance on a benchmark dataset cvpr polarimetric multi view stereo z cui j gu b shi p tan j kautz ieee conference on computer vision and pattern recognition cvpr july 2017 pages 369 378 pdf supplemental video more multi view stereo relies on feature correspondences for 3d reconstruction and thus is fundamentally flawed in dealing with featureless scenes in this paper we propose polarimetric multi view stereo which combines per pixel photometric information from polarization with epipolar constraints from multiple views for 3d reconstruction polarization reveals surface normal information and is thus helpful to propagate depth to featureless regions polarimetric multi view stereo is completely passive and can be applied outdoors in uncontrolled illumination since the data capture can be done simply with either a polarizer or a polarization camera unlike previous work on shape from polarization which is limited to either diffuse polarization or specular polarization only we propose a novel polarization imaging model that can handle real world objects with mixed polarization we prove there are exactly two types of ambiguities on estimating surface azimuth angles from polarization and we resolve them with graph optimization and iso depth contour tracing this step significantly improves the initial depth map estimate which are later fused together for complete 3d reconstruction extensive experimental results demonstrate high quality 3d reconstruction and better performance than state of the art multi view stereo methods especially on featureless 3d objects such as ceramic tiles office room with white walls and highly reflective cars in the outdoors iclr ga3c gpu based a3c for deep reinforcement learning m babaeizadeh i frosio s tyree j clemons j kautz international conference on learning representations april 2017 pdf code more we introduce and analyze the computational aspects of a hybrid cpu gpu implementation of the asynchronous advantage actor critic a3c algorithm currently the state of the art method in reinforcement learning for various gaming tasks our analysis concentrates on the critical aspects to leverage the gpuâ s computational power including the introduction of a system of queues and a dynamic scheduling strategy potentially helpful for other asynchronous algorithms as well we also show the potential for the use of larger dnn models on a gpu our tensor flow implementation achieves a significant speed up compared to our cpu only implementation and it will be made publicly available to other researchers iclr pruning convolutional neural networks for resource efficient transfer learning p molchanov s tyree t karras t aila j kautz international conference on learning representations april 2017 pdf more we propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference focusing on transfer learning where large and potentially unwieldy pretrained networks are adapted to specialized tasks we interleave greedy criteria based pruning with fine tuning by backpropagation a computationally efficient procedure that maintains good generalization in the pruned network we propose a new criterion based on an efficient first order taylor expansion to approximate the absolute change in training cost induced by pruning a network component after normalization the proposed criterion scales appropriately across all layers of a deep cnn eliminating the need for per layer sensitivity analysis the proposed criterion demonstrates superior performance compared to other criteria such as the norm of kernel weights or average feature map activation ieee tci loss functions for neural networks for image processing h zhao o gallo i frosio j kautz ieee transactions on computational imaging 3 1 march 2017 pages 47 57 ieee dl arxiv more neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems the impact of the loss layer of neural networks however has not received much attention in the context of image processing the default and virtually only choice is l2 in this paper we bring attention to alternative choices we study the performance of several losses including perceptually motivated losses and propose a novel differentiable error function we show that the quality of the results improves significantly with better loss functions even when the network architecture is left unchanged 2016 siggraph asia computational bounce flash for indoor portraits l murmann a davis j kautz f durand acm transactions on graphics proceedings siggraph asia 2016 35 6 december 2016 pages 190 1 190 9 pdf supplemental www more portraits taken with direct flash look harsh and unflattering because the light source comes from a small set of angles very close to the camera advanced photographers address this problem by using bounce flash a technique where the flash is directed towards other surfaces in the room creating a larger virtual light source that can be cast from different directions to provide better shading variation for 3d modeling however finding the right direction to point a bounce flash requires skill and careful consideration of the available surfaces and subject configuration inspired by the impact of automation for exposure focus and flash metering we automate control of the flash direction for bounce illumination we first identify criteria for evaluating flash directions based on established photography literature and relate these criteria to the color and geometry of a scene we augment a camera with servomotors rotate the flash head and additional sensors a fisheye and 3d sensors to gather information about potential bounce surfaces we present a simple numerical optimization criterion that finds directions for the flash that consistently yield compelling illumination and demonstrate the effectiveness of our various criteria in common photographic configurations nipsw ga3c gpu based a3c for deep reinforcement learning ...
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