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d simulated exposure fusion qfsef method by factorizing an input image into multiple illumination consistent layers to this end we use an iterative sparse matrix factorization scheme by representing the image as a two dimensional pure quaternion matrix theoretically our representation is based on the dichromatic reflection model and accounts for the two scene illumination characteristics by factorizing each progressively generated image into separate specular and diffuse components we empirically prove the advantages of our factorization scheme over other exposure simulation methods by using it for the low light image enhancement task inproceedings sainiqfsef22 author saini saurabh and narayanan p j title quaternion factorized simulated exposure fusion for low light image enhancement year 2022 doi 10 1145 3571600 3571604 booktitle proceedings of the thirteenth indian conference on computer vision graphics and image processing series icvgip 22 interpreting intrinsic image decomposition using concept activations avani gupta saurabh saini p j narayanan icvgip 2022 oral best paper award abstract bibtex webpage code poster supplementary evaluation of ill posed problems like intrinsic image decomposition iid is challenging iid involves decomposing an image into its constituent illumination invariant reflectance r and albedo invariant shading s components contemporary iid methods use deep learning models and require large datasets for training the evaluation of iid is carried out on either synthetic ground truth images or sparsely annotated natural images a scene can be split into reflectance and shading in multiple valid ways comparison with one specific decomposition in the ground truth images used by current iid evaluation metrics like lmse mse dssim whdr saw ap etc is inadequate measuring r s disentanglement is a better way to evaluate the quality of iid inspired by ml interpretability methods we propose concept sensitivity metrics csm that directly measure disentanglement using sensitivity to relevant concepts inproceedings guptaiid22 author gupta avani and saini saurabh and narayanan p j title interpreting intrinsic image decomposition using concept activations year 2022 doi 10 1145 3571600 3571603 booktitle proceedings of the thirteenth indian conference on computer vision graphics and image processing series icvgip 22 styletrf stylizing tensorial radiance fields rahul goel sirikonda dhawal saurabh saini p j narayanan icvgip 2022 spotlight abstract bibtex webpage arxiv poster video stylized view generation of scenes captured casually using a camera has received much attention recently the geometry and appearance of the scene are typically captured as neural point sets or neural radiance fields in the previous work an image stylization method is used to stylize the captured appearance by training its network jointly or iteratively with the structure capture network the state of the art snerf method trains the nerf and stylization network in an alternating manner these methods have high training time and require joint optimization in this work we present styletrf a compact quick to optimize strategy for stylized view generation using tensorf the appearance part is finetuned using sparse stylized priors of a few views rendered using the tensorf representation for a few iterations our method thus effectively decouples style adaption from view capture and is much faster than the previous methods we show state of the art results on several scenes used for this purpose inproceedings goel2022styletrf author goel rahul and sirikonda dhawal and saini saurabh and narayanan p j title styletrf stylizing tensorial radiance fields year 2022 doi 10 1145 3571600 3571643 booktitle proceedings of the thirteenth indian conference on computer vision graphics and image processing series icvgip 22 learning to stylize novel views hsin ping huang hung yu tseng saurabh saini maneesh singh ming hsuan yang iccv 2021 poster abstract bibtex webpage code arxiv supplementary we tackle a 3d scene stylization problem generating stylized images of a scene from arbitrary novel views given a set of images of the same scene and a reference image of the desired style as inputs direct solution of combining novel view synthesis and stylization approaches lead to results that are blurry or not consistent across different views we propose a point cloud based method for consistent 3d scene stylization first we construct the point cloud by back projecting the image features to the 3d space second we develop point cloud aggregation modules to gather the style information of the 3d scene and then modulate the features in the point cloud with a linear transformation matrix finally we project the transformed features to 2d space to obtain the novel views experimental results on two diverse datasets of real world scenes validate that our method generates consistent stylized novel view synthesis results against other alternative approaches article huang2021learningts title learning to stylize novel views author hsin ping huang and hung yu tseng and saurabh saini and maneesh kumar singh and ming hsuan yang journal 2021 ieee cvf international conference on computer vision iccv year 2021 pages 13849 13858 semantic hierarchical priors for intrinsic image decomposition saurabh saini p j narayanan arxiv 2019 abstract bibtex arxiv intrinsic image decomposition iid is a challenging and interesting computer vision problem with various applications in several fields we present novel semantic priors and an integrated approach for single image iid that involves analyzing image at three hierarchical context levels local context priors capture scene properties at each pixel within a small neighbourhood mid level context priors encode object level semantics global context priors establish correspondences at the scene level our semantic priors are designed on both fixed and flexible regions using selective search method and convolutional neural network features our iid method is an iterative multistage optimization scheme and consists of two complementary formulations l2 smoothing for shading and l1 sparsity for reflectance experiments and analysis of our method indicate the utility of our semantic priors and structured hierarchical analysis in an iid framework we compare our method with other contemporary iid solutions and show results with lesser artifacts finally we highlight that proper choice and encoding of prior knowledge can produce competitive results even when compared to end to end deep learning iid methods signifying the importance of such priors we believe that the insights and techniques presented in this paper would be useful in the future iid research article saini2019semantichp title semantic hierarchical priors for intrinsic image decomposition author saurabh saini and p j narayanan journal arxiv year 2019 volume abs 1902 03830 neural rendering for material visualization aakash kt parikshit sakurikar saurabh saini p j narayanan siggraph asia 2019 technical briefs abstract bibtex webpage code arxiv photo realism in computer generated imagery is crucially dependent on how well an artist is able to recreate real world materials in the scene the workflow for material modeling and editing typically involves manual tweaking of material parameters and uses a standard path tracing engine for visual feedback a lot of time may be spent in iterative selection and rendering of materials at an appropriate quality in this work we propose a convolutional neural network based workflow which quickly generates high quality ray traced material visualizations on a shaderball our novel architecture allows for control over environment lighting and assists material selection along with the ability to render spatially varying materials additionally our network enables control over environment lighting which gives an artist more freedom and provides better visualization of the rendered material comparison with state of the art denoising and neural rendering techniques suggests that our neural renderer performs faster and better we provide a interactive visualization tool and release our training dataset to foster further research in this area article 2019arxiv190809530k author kt aakash and sakurikar parikshit and saini saurabh and narayanan p j title a flexible neural renderer for material visualization journal arxiv e prints keywords computer science graphics year 2019 month aug archiveprefix arxiv eprint 1908 09530 primaryclass cs gr semantic priors for intrinsic image decomposition saurabh saini p j narayanan bmvc 2018 oral best industrial paper honourable mention abstract bibtex pdf supplementary intrinsic image decomposition iid is a challenging and interesting computer vision problem with various applications in several fields we present novel semantic priors and an integrated approach for single image iid that involves analyzing image at three hierarchical context levels local context priors capture scene properties at each pixel within a small neighborhood mid level context priors encode object level semantics global context priors establish correspondences at the scene level our semantic priors are designed on both fixed and flexible regions using selective search method and convolutional neural network features experiments and analysis of our method indicate the utility of our weak semantic priors and structured hierarchical analysis in an iid framework we compare our method with the current state of the art and show results with lesser artifacts finally we highlight that proper choice and encoding of prior knowledge can produce competitive results compared to end to end deep learning iid methods signifying the importance of such priors we believe that the insights and techniques presented in this paper would be useful in the future iid research inproceedings dblp conf bmvc sainin18 author saurabh saini and p j narayanan title semantic priors for intrinsic image decomposition booktitle british machine vision conference 2018 bmvc 2018 northumbria university newcastle uk september 3 6 2018 pages 154 year 2018 url http bmvc2018 org contents papers 0796 pdf human shape capture and tracking at home gaurav mishra saurabh saini kiran varanasi p j narayanan wacv 2018 oral abstract bibtex webpage pdf supplementary presentation human body tracking typically requires specialized capture set ups although pose tracking is available in consumer devices like microsoft kinect it is restricted to stick figures visualizing body part detection in this paper we propose a method for full 3d human body shape and motion capture of arbitrary movements from the depth channel of a single kinect when the subject wears casual clothes we do not use the rgb channel or an initialization procedure that requires the subject to move around in front of the camera this makes our method applicable for arbitrary clothing textures and lighting environments with minimal subject intervention our method consists of 3d surface feature detection and articulated motion tracking which is regularized by a statistical human body model we also propose the idea of a consensus mesh cmesh which is the 3d template of a person created from a single view point we demonstrate tracking results on challenging poses and argue that using cmesh along with statistical body models can improve tracking accuracies quantitative evaluation of our dense body tracking shows that our method has very little drift which is improved by the usage of cmesh inproceedings dblp conf wacv mishrasvn18 author gaurav mishra and saurabh saini and kiran varanasi and p j narayanan title human shape capture and tracking at home booktitle 2018 ieee winter conference on applications of computer vision wacv 2018 lake tahoe nv usa march 12 15 2018 pages 390 399 year 2018 doi 10 1109 wacv 2018 00049 intrinsic image decomposition using focal stacks saurabh saini parikshit sakurikar p j narayanan icvgip 2016 oral abstract bibtex code pdf supplementary in this paper we presents a novel method rgbf iid for intrinsic image decomposition of a wild scene without any restrictions on the complexity illumination or scale of the image we use focal stacks of the scene as input a focal stack captures a scene at varying focal distances since focus depends on distance to the object this representation has information beyond an rgb image towards an rgbd image with depth we call our representation an rgbf image to highlight this we use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack these maps are used to define sparse local and global pixel neighbourhoods adhering to the structure of the underlying 3d scene we use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system we present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions we also test our system on a larger dataset of synthetically generated focal stacks from nyuv2 and mpi sintel datasets and show competitive performance against current state of the art iid methods that use rgbd images our method provides a strong evidence for the potential of rgbf modality in place of rgbd in computer vision inproceedings dblp conf icvgip sainisn16 author saurabh saini and parikshit sakurikar and p j narayanan title intrinsic image decomposition using focal stacks booktitle proceedings of the tenth indian conference on computer vision graphics and image processing icvgip 2016 guwahati assam india december 18 22 2016 pages 88 1 88 8 year 2016 url https doi org 10 1145 3009977 3010046 doi 10 1145 3009977 3010046 learning to hash tag videos with tag2vec aditya singh saurabh saini rajvi shah p j narayanan icvgip 2016 oral abstract bibtex arxiv user given tags or labels are valuable resources for semantic understanding of visual media such as images and videos recently a new type of labeling mechanism known as hash tags have become increasingly popular on social media sites in this paper we study the problem of generating relevant and useful hash tags for short video clips traditional data driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation we attempt to learn a direct low cost mapping from video to hash tags using a two step training process we first employ a natural language processing nlp technique skip gram models with neural network training to learn a low dimensional vector representation of hash tags tag2vec using a corpus of 10 million hash tags we then train an embedding function to map video features to the low dimensional tag2vec space we learn this embedding for 29 categories of short video clips with hash tags a query video without any tag information can then be directly mapped to the vector space of tags using the learned embedding and rel...
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