Search Results for author: Varun Jampani

Found 57 papers, 27 papers with code

Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues

no code implementations21 Apr 2022 Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James M. Rehg

We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image.

3D Shape Reconstruction 3D Shape Representation +1

LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity

no code implementations6 Apr 2022 Tejan Karmali, Abhinav Atrishi, Sai Sree Harsha, Susmit Agrawal, Varun Jampani, R. Venkatesh Babu

Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image, which are further used to learn landmarks in a semi-supervised manner.

Self-Supervised Learning

Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation

no code implementations NeurIPS 2021 Jogendra Nath Kundu, Siddharth Seth, Anirudh Jamkhandi, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu

To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target.

3D Human Pose Estimation

Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery

no code implementations NeurIPS 2021 Mugalodi Rakesh, Jogendra Nath Kundu, Varun Jampani, R. Venkatesh Babu

Articulation-centric 2D/3D pose supervision forms the core training objective in most existing 3D human pose estimation techniques.

3D Human Pose Estimation

Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation

no code implementations9 Feb 2022 Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri, Varun Jampani, R. Venkatesh Babu

However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination.

Disentanglement Domain Adaptation +1

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction

1 code implementation NeurIPS 2021 Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu, Deva Ramanan

The surface embeddings are implemented as coordinate-based MLPs that are fit to each video via consistency and contrastive reconstruction losses. Experimental results show that ViSER compares favorably against prior work on challenging videos of humans with loose clothing and unusual poses as well as animals videos from DAVIS and YTVOS.

3D Shape Reconstruction 3D Shape Reconstruction from Videos +2

Robust Visual Reasoning via Language Guided Neural Module Networks

no code implementations NeurIPS 2021 Arjun Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu

Neural module networks (NMN) are a popular approach for solving multi-modal tasks such as visual question answering (VQA) and visual referring expression recognition (REF).

Question Answering Referring Expression +3

Approximate Bijective Correspondence for isolating factors of variation

no code implementations29 Sep 2021 Kieran A Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

We propose a novel algorithm that relies on a weak form of supervision where the data is partitioned into sets according to certain \textit{inactive} factors of variation.

Contrastive Learning Data Augmentation +1

SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting

no code implementations ICCV 2021 Varun Jampani, Huiwen Chang, Kyle Sargent, Abhishek Kar, Richard Tucker, Michael Krainin, Dominik Kaeser, William T. Freeman, David Salesin, Brian Curless, Ce Liu

We present SLIDE, a modular and unified system for single image 3D photography that uses a simple yet effective soft layering strategy to better preserve appearance details in novel views.

Image Matting

Discovering 3D Parts from Image Collections

no code implementations ICCV 2021 Chun-Han Yao, Wei-Chih Hung, Varun Jampani, Ming-Hsuan Yang

Reasoning 3D shapes from 2D images is an essential yet challenging task, especially when only single-view images are at our disposal.

Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

2 code implementations10 Jun 2021 Kieran Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses.

3D Pose Estimation 3D Rotation Estimation

Decoupled Dynamic Filter Networks

1 code implementation CVPR 2021 Jingkai Zhou, Varun Jampani, Zhixiong Pi, Qiong Liu, Ming-Hsuan Yang

Inspired by recent advances in attention, DDF decouples a depth-wise dynamic filter into spatial and channel dynamic filters.

Image Classification

AutoFlow: Learning a Better Training Set for Optical Flow

no code implementations CVPR 2021 Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, Ce Liu

Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications.

Optical Flow Estimation

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation

2 code implementations CVPR 2021 Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, Joongkyu Kim

By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation.

Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision

1 code implementation4 Mar 2021 Kieran A. Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

We propose a novel algorithm that utilizes a weak form of supervision where the data is partitioned into sets according to certain inactive (common) factors of variation which are invariant across elements of each set.

Data Augmentation Pose Transfer

Leveraging affinity cycle consistency to isolate factors of variation in learned representations

no code implementations1 Jan 2021 Kieran A Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

In this work, we operate in the setting where limited information is known about the data in the form of groupings, or set membership, and the task is to learn representations which isolate the factors of variation that are common across the groupings.

Pose Transfer Representation Learning

Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image

1 code implementation ICCV 2021 Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, Angjoo Kanazawa

We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image.

Image Generation Video Generation

NeRD: Neural Reflectance Decomposition from Image Collections

1 code implementation ICCV 2021 Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, Hendrik P. A. Lensch

This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination.

Improving Deep Stereo Network Generalization with Geometric Priors

no code implementations25 Aug 2020 Jialiang Wang, Varun Jampani, Deqing Sun, Charles Loop, Stan Birchfield, Jan Kautz

End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar.

Generative View Synthesis: From Single-view Semantics to Novel-view Images

no code implementations NeurIPS 2020 Tewodros Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker

We propose to push the envelope further, and introduce Generative View Synthesis (GVS), which can synthesize multiple photorealistic views of a scene given a single semantic map.

Image Generation Translation

Appearance Consensus Driven Self-Supervised Human Mesh Recovery

no code implementations ECCV 2020 Jogendra Nath Kundu, Mugalodi Rakesh, Varun Jampani, Rahul Mysore Venkatesh, R. Venkatesh Babu

We present a self-supervised human mesh recovery framework to infer human pose and shape from monocular images in the absence of any paired supervision.

3D Pose Estimation Human Mesh Recovery

Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition

no code implementations29 Jun 2020 Hassan Abu Alhaija, Siva Karthik Mustikovela, Justus Thies, Varun Jampani, Matthias Nießner, Andreas Geiger, Carsten Rother

Neural rendering techniques promise efficient photo-realistic image synthesis while at the same time providing rich control over scene parameters by learning the physical image formation process.

Image-to-Image Translation Intrinsic Image Decomposition +1

From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks

1 code implementation CVPR 2020 K L Navaneet, Ansu Mathew, Shashank Kashyap, Wei-Chih Hung, Varun Jampani, R. Venkatesh Babu

We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner, making use of differentiable point cloud renderer to train with 2D supervision.

3D Object Reconstruction From A Single Image 3D Point Cloud Reconstruction +2

Two-shot Spatially-varying BRDF and Shape Estimation

1 code implementation CVPR 2020 Mark Boss, Varun Jampani, Kihwan Kim, Hendrik P. A. Lensch, Jan Kautz

Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.

Self-supervised Single-view 3D Reconstruction via Semantic Consistency

no code implementations ECCV 2020 Xueting Li, Sifei Liu, Kihwan Kim, Shalini De Mello, Varun Jampani, Ming-Hsuan Yang, Jan Kautz

To the best of our knowledge, we are the first to try and solve the single-view reconstruction problem without a category-specific template mesh or semantic keypoints.

3D Reconstruction Single-View 3D Reconstruction

SENSE: a Shared Encoder Network for Scene-flow Estimation

1 code implementation ICCV 2019 Huaizu Jiang, Deqing Sun, Varun Jampani, Zhaoyang Lv, Erik Learned-Miller, Jan Kautz

We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation.

Disparity Estimation Occlusion Estimation +3

Learning Propagation for Arbitrarily-structured Data

no code implementations ICCV 2019 Sifei Liu, Xueting Li, Varun Jampani, Shalini De Mello, Jan Kautz

We experiment with semantic segmentation networks, where we use our propagation module to jointly train on different data -- images, superpixels and point clouds.

Point Cloud Segmentation Semantic Segmentation +1

Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

3 code implementations ICCV 2019 Towaki Takikawa, David Acuna, Varun Jampani, Sanja Fidler

Here, we propose a new two-stream CNN architecture for semantic segmentation that explicitly wires shape information as a separate processing branch, i. e. shape stream, that processes information in parallel to the classical stream.

Semantic Segmentation

SCOPS: Self-Supervised Co-Part Segmentation

1 code implementation CVPR 2019 Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, Jan Kautz

Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations.

Pixel-Adaptive Convolutional Neural Networks

2 code implementations CVPR 2019 Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, Jan Kautz

In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.

Superpixel Sampling Networks

2 code implementations ECCV 2018 Varun Jampani, Deqing Sun, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz

Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks.

Superpixels

Learning Superpixels With Segmentation-Aware Affinity Loss

no code implementations CVPR 2018 Wei-Chih Tu, Ming-Yu Liu, Varun Jampani, Deqing Sun, Shao-Yi Chien, Ming-Hsuan Yang, Jan Kautz

Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning.

Superpixels

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

1 code implementation CVPR 2019 Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.

Monocular Depth Estimation Motion Estimation +2

Switchable Temporal Propagation Network

1 code implementation ECCV 2018 Sifei Liu, Guangyu Zhong, Shalini De Mello, Jinwei Gu, Varun Jampani, Ming-Hsuan Yang, Jan Kautz

Our approach is based on a temporal propagation network (TPN), which models the transition-related affinity between a pair of frames in a purely data-driven manner.

Frame Video Compression

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

2 code implementations CVPR 2018 Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, Jan Kautz

We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.

3D Part Segmentation 3D Semantic Segmentation

On the Integration of Optical Flow and Action Recognition

no code implementations22 Dec 2017 Laura Sevilla-Lara, Yiyi Liao, Fatma Guney, Varun Jampani, Andreas Geiger, Michael J. Black

Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better.

Action Recognition Optical Flow Estimation

Learning Inference Models for Computer Vision

no code implementations31 Aug 2017 Varun Jampani

We propose inference techniques for both generative and discriminative vision models.

Bayesian Inference

Semantic Video CNNs through Representation Warping

1 code implementation ICCV 2017 Raghudeep Gadde, Varun Jampani, Peter V. Gehler

A key insight of this work is that fast optical flow methods can be combined with many different CNN architectures for improved performance and end-to-end training.

Optical Flow Estimation Semantic Segmentation

Efficient 2D and 3D Facade Segmentation using Auto-Context

no code implementations21 Jun 2016 Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter V. Gehler

This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades.

Superpixel Convolutional Networks using Bilateral Inceptions

no code implementations20 Nov 2015 Raghudeep Gadde, Varun Jampani, Martin Kiefel, Daniel Kappler, Peter V. Gehler

We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image.

Semantic Segmentation Superpixels

Permutohedral Lattice CNNs

no code implementations20 Dec 2014 Martin Kiefel, Varun Jampani, Peter V. Gehler

This paper presents a convolutional layer that is able to process sparse input features.

Consensus Message Passing for Layered Graphical Models

no code implementations27 Oct 2014 Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn

Generative models provide a powerful framework for probabilistic reasoning.

The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

1 code implementation4 Feb 2014 Varun Jampani, Sebastian Nowozin, Matthew Loper, Peter V. Gehler

Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion.

Bayesian Inference

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