Neural reflectance decomposition from unconstrained image collections with a novel illumination representation and pre-integration network.
M. Boss, V. Jampani, R. Braun, C. Liu, J. T. Barron, H. Lensch
Neural Information Processing Systems, NeurIPS’21
pdf / project page / video / code (github)
Category-agnostic and template-free learning of articulated 3D reconstruction and dense 3D trajectories from a monocular video.
G. Yang, D. Sun, V. Jampani, D. Vlasic, F. Cole, C. Liu, D. Ramanan
Neural Information Processing Systems, NeurIPS’21
A novel language-adaptive convolution layer for tackling vision-language tasks such as VQA and REF.
A. Akula, V. Jampani, S. Changpinyo, S. C. Zhu
Neural Information Processing Systems, NeurIPS’21
3D human pose learning with silhouette supervision using a differentiable topological-skeleton extraction.
M. Rakesh*, J. N. Kundu*, V. Jampani, R. V. Babu (*equal contribution)
Neural Information Processing Systems, NeurIPS’21
pdf / project page / video
Domain adaption like technique to learn 3D pose estimation given only unpaired videos and 3D pose datasets.
J. N. Kundu, S. Seth, A. Jamkhandi, Y. Pradyumna, V. Jampani, R. V. Babu, A. Chakraborty
Neural Information Processing Systems, NeurIPS’21
A fast, modular and unified network for single image 3D photography that can preserve intricate hair-like structures.
V. Jampani*, H. Chang*, K. Sargent, A. Kar, R. Tucker, M. Krainin, D. Kaeser, W. T. Freeman, D. Salesin, B. Curless, C. Liu (*equal contribution)
International Conference on Computer Vision, ICCV’21 (oral)
pdf / project page / video / results
Estimating re-lightable 3D asset (shape and material properties) from the image collection of an object captured under unconstrained illumination conditions.
M. Boss, R. Braun, V. Jampani, J. T. Barron, C. Liu, H. Lensch
International Conference on Computer Vision, ICCV’21
pdf / project page / video / code (soon)
Perpetual novel view generation corresponding to an arbitrarily long camera trajectory given a single image of a natural scene.
A. Liu*, R. Tucker*, V. Jampani, A. Makadia, N. Snavely, A. Kanazawa (*equal contribution)
International Conference on Computer Vision, ICCV’21 (oral)
pdf / project page / video / code (github)
Learning 3D shape reconstuction while discovering constituent 3D parts from category-specific and posed 2D image collections.
C-H. Yao, W-C. Hung, V. Jampani, M-H. Yang
International Conference on Computer Vision, ICCV’21
pdf / project page / video / code (soon)
Multi-head network with multiple augmentations for domain generalization and adaptation for semantic segmentation.
J. N. Kundu*, A. Kulkarni*, A. Singh, V. Jampani, R. V. Babu (*equal contribution)
International Conference on Computer Vision, ICCV’21
A method to predict probability distributions over the rotation manifold that is particularly useful for pose estimation of symmetric objects.
K. Murphy*, C. Esteves*, V. Jampani, S. Ramalingam, A. Makadia (*equal contribution)
International Conference on Machine Learning, ICML’21 (spotlight)
pdf / project page / video / code (github)
DDF is a lighter-weight, high-performing content-adaptive convolution layer that can readily replace standard convolution layers in CNNs.
J. Zhou, V. Jampani, Z. Pi, Q. Liu, M-H. Yang.
Computer Vision and Pattern Recognition, CVPR’21
pdf / project page / video / code (github)
Template-free and category-agnostic articulated 3D shape reconstruction from a given monocular video.
G. Yang, D. Sun, V. Jampani, D. Vlasic, F. Cole, H. Chang, D. Ramanan, W. T. Freeman, C. Liu
Computer Vision and Pattern Recognition, CVPR’21
Jointly rendering training data while learning flow model can result in state-of-the-art pre-training and fine-tuning optical flow results.
D. Sun, D. Vlasic, C. Hermann, V. Jampani, M. Krainin, H. Chang, R. Zabih, W. T. Freeman, C. Liu
Computer Vision and Pattern Recognition, CVPR’21 (oral)
Few-shot segmentation with superpixel guided adaptive prototype learning and allocation.
G. Li, V. Jampani, L. Sevilla-Lara, D.Sun, J. Kim, J. Kim
Computer Vision and Pattern Recognition, CVPR’21
pdf / project page / video / code (github)
Learning to generate semantically consistent novel-view images from a given single 2D semantics.
T. A. Habtegebrial, V. Jampani, O. Gallo, D. Stricker
Neural Information Processing Systems, NeurIPS’20
pdf / video / project page / code (github)
Learning human mesh recovery from unannotated video frames and unpaired pose data.
J. N. Kundu*, M. Rakesh*, V. Jampani, R. M. Venkatesh, R. V. Babu (*equal contribution)
European Conference on Computer Vision, ECCV’20 (oral)
pdf / video / project page / code (github)
Learning 3D object reconstruction from unannotated image collections via self-supervised semantic consistency.
X. Li, S. Liu, K. Kim, S. D. Mello, V. Jampani, M. H. Yang and J. Kautz
European Conference on Computer Vision, ECCV’20
pdf / video / project page / code (github)
First learning-based point cloud registration method that explicitly leverages a probabilistic registration paradigm.
W. Yuan, B. Eckart, K. Kim, V. Jampani, D. Fox and J. Kautz
European Conference on Computer Vision, ECCV’20
pdf / video / project page / code (github)
Learning 3D object viewpoint from unannotated image collections via self-supervision.
S. K. Mustikovela, V. Jampani, S. D. Mello, S. Liu, U. Iqbal, C. Rother and J. Kautz
Computer Vision and Pattern Recognition, CVPR’20
pdf / video / project page / code (github)
Practical SVBRDF and shape estimation with two-shot mobile capture.
M. Boss, V. Jampani, K. Kim, H. Lensch and J. Kautz
Computer Vision and Pattern Recognition, CVPR’20
pdf / video / project page / code (github)
3D human pose estimation technique that is self-supervised using image pairs from in-the-wild videos.
J. N. Kundu*, S. Seth*, V. Jampani, M. Rakesh, R. V. Babu and A. Chakraborty (*equal contribution)
Computer Vision and Pattern Recognition, CVPR’20 (oral)
Point cloud reconstruction network trained with self-supervision using image collections.
K. L. Navaneet, A. Mathew, S. Kashyap, W-C. Hung, V. Jampani and R. V. Babu
Computer Vision and Pattern Recognition, CVPR’20
A generalized spatial propagation network module that learns to propagate information across arbitrarily-structured data such as superpixels and point clouds.
S. Liu, X. Li, V. Jampani, S. D. Mello and J. Kautz
Internationl Conference on Computer Vision, ICCV’19
A compact shared-encoder network along with semi-supervised loss functions for depth and optical flow estimation.
H. Jiang, D. Sun, V. Jampani, Z. Lv, E. Learned-Miller and J. Kautz
Internationl Conference on Computer Vision, ICCV’19 (oral)
State-of-the-art semantic segmentation with two-stream CNN architecture that explicitly wires shape information as separate processing branch.
T. Takikawa*, D. Akuna*, V. Jampani and S. Fidler (*equal contribution)
International Conference on Computer Vision, ICCV’19
pdf / code (github) / project page / video
Generalization of spatially-invariant convolutions to spatially-varying convolutions with applications in joint-image upsampling, conditional random fields and layer hot-swapping.
H. Su, V. Jampani, D. Sun, O. Gallo, E. Learned-Miller and J. Kautz
Computer Vision and Pattern Recognition, CVPR’19
pdf / poster / video / project page / code (github)
Learning to discover semantically consistent object part segments from image collections.
W-C. Hung, V. Jampani, S. Liu, P. Molchanov, M-H. Yang and J. Kautz
Computer Vision and Pattern Recognition, CVPR’19
pdf / poster / video / project page / code (github)
A general unsupervised deep learning framework for learning depth, optical flow, camera motion and motion segmentation from videos.
A. Ranjan, V. Jampani, L. Balles, K. Kim, D. Sun, J. Wulff and M. J. Black
Computer Vision and Pattern Recognition, CVPR’19
Single Image 3D object reconstruction along with texture, segmentation and normal prediction with differentiable feature rendering.
K. L. Navaneet, P. Mandikal, V. Jampani and R. V. Babu
3D-WiDGET, CVPR-Workshops’19 (Oral)
An end-to-end trainable deep superpixel algorithm that allows learning with flexible loss functions resulting in the learning of task-specific superpixels.
V. Jampani, D. Sun, M-Y. Liu, M-H. Yang and J. Kautz
European Conference on Computer Vision, ECCV’18
pdf / poster / video / project page / code (github)
A generic fast deep learning framework for propagating a variety of visual properties (such as color, HDR, segmentation) across video frames.
S. Liu, G. Zhong, S. D. Mello, J. Gu, V. Jampani, M-H. Yang and J. Kautz
European Conference on Computer Vision, ECCV’18
An investigate study on why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better.
L. Sevilla, Y. Liao, F. Güney, V. Jampani, A. Geiger and M. J. Black
German Conference on Pattern Recognition, GCPR’18 (oral)
A fast and end-to-end trainable neural network that directly works on point clouds and can also do joint 2D-3D processing.
H. Su, V. Jampani, D. Sun, S. Maji, E. Kalogerakis, M-H. Yang and J. Kautz
Computer Vision and Pattern Recognition, CVPR’18 (oral, best paper honorable mention, NVAIL Pioneering Research Award)
pdf / poster / video / CVPR talk / code (github)
Variable-length multi-frame video interpolation via self-supervised optical flow estimation and occlusion reasoning.
H. Jiang, D. Sun, V. Jampani, M-H. Yang, E. Learned-Miller and J. Kautz
Computer Vision and Pattern Recognition, CVPR’18 (spotlight)
pdf / supplementary / news / CVPR talk / video results
A learning technique that leverages deep networks to predict pixel affinities useful for graph-based superpixel segmentation.
W-C. Tu, M-Y. Liu, V. Jampani, D. Sun, S-Y. Chien, M-H. Yang and J. Kautz
Computer Vision and Pattern Recognition, CVPR’18
A study exploring the use of domain randomization for real-world computer vision tasks such as object detection.
J. Tremblay*, A. Prakash*, D. Acuna*, M. Brophy*, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon and S. Birchfield (*equal contribution)
Workshop on Autonomous Driving, CVPR-Workshops’18
A fast and end-to-end trainable approach for converting image CNNs to video CNNs for semantic segmentation.
R. Gadde, V. Jampani and P. V. Gehler
International Conference on Computer Vision, ICCV’17 (oral)
pdf / video / code (github) / ICCV talk / poster
A fast, generic and end-to-end trainable approach for propagating information across video frames.
V. Jampani, R. Gadde and P. V. Gehler
Computer Vision and Pattern Recognition, CVPR’17
pdf / supplementary / project page / code (github) / poster
A fast auto-context based facade segmentation approach for segmenting both 2D images and 3D point clouds.
R. Gadde*, V. Jampani*, R. Marlet and P. V. Gehler (*equal contribution)
Pattern Analysis and Machine Intelligence, PAMI’17
An inception style fast bilateral filtering module that can be used in existing image segmentation CNNs.
R. Gadde*, V. Jampani*, M. Kiefel, D. Kappler and P. V. Gehler (*equal contribution)
European Conference on Computer Vision, ECCV’16
pdf / supplementary / project page / code (github) / poster
Parameterization and learning of sparse high-dimensional filters with wide range of applications in image filtering, CRFs and neural networks.
V. Jampani, M. Kiefel and P. V. Gehler
Computer Vision and Pattern Recognition, CVPR’16
pdf / supplementary / project page / code (github) / poster
An approach for incorporating semantics of the scene for better optical flow estimation.
L. Sevilla, D. Sun, V. Jampani and M. J. Black
Computer Vision and Pattern Recognition, CVPR’16
pdf / video / project page / code (zip)
CNNs with high-dimensional permutohedral kernels instead of standard spatial kernels.
M. Kiefel, V. Jampani and P. V. Gehler
International Conference on Learning Representations, ICLR Workshops’15
Improved message passing inference in layered vision models that can be used in probabilistic programs like Infer.NET.
V. Jampani*, S. M. A. Eslami*, D. Tarlow, P. Kohli and J. Winn (*equal contribution)
Artificial Intelligence and Statistics, AISTATS’15
pdf / supplementary / arXiv
A state-of-the-art approach for facade segmentation that is faster than most existing approaches.
V. Jampani*, R. Gadde* and P. V. Gehler (*equal contribution)
Winter Conference on Applications of Computer Vision, WACV’15
An experimental eye-tracking study on X-ray image perception, with observers ranging from novices to experienced radiologists.
V. Jampani, V. Vaidya, J. Sivaswamy and K. L. Tourani
SPIE, Medical Imaging’11
A novel image search user interface that explores a different alternative to the traditional grid interfaces.
V. Jampani, G. Ramos and S. Drucker
Microsoft Research Technial Report, Redmond, MSR-TR-2010-148
Learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks.
V. Jampani
PhD Thesis, MPI for Intelligent Systems and University of Tübingen, December, 2016
Eye tracking experimental studies and models for X-Ray Image Perception and Diagnosis.
V. Jampani
Master Thesis, IIIT-Hyderabad, January, 2013