Search Results for author: Kalyan Sunkavalli

Found 45 papers, 10 papers with code

RigNeRF: Fully Controllable Neural 3D Portraits

no code implementations CVPR 2022 ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu

In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video.

Face Model Neural Rendering +1

TileGen: Tileable, Controllable Material Generation and Capture

no code implementations12 Jun 2022 Xilong Zhou, Miloš Hašan, Valentin Deschaintre, Paul Guerrero, Kalyan Sunkavalli, Nima Kalantari

The resulting materials are tileable, can be larger than the target image, and are editable by varying the condition.

Differentiable Rendering of Neural SDFs through Reparameterization

no code implementations10 Jun 2022 Sai Praveen Bangaru, Michaël Gharbi, Tzu-Mao Li, Fujun Luan, Kalyan Sunkavalli, Miloš Hašan, Sai Bi, Zexiang Xu, Gilbert Bernstein, Frédo Durand

Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.

Physically-Based Editing of Indoor Scene Lighting from a Single Image

no code implementations19 May 2022 Zhengqin Li, Jia Shi, Sai Bi, Rui Zhu, Kalyan Sunkavalli, Miloš Hašan, Zexiang Xu, Ravi Ramamoorthi, Manmohan Chandraker

We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions.

Neural Rendering

NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction

no code implementations CVPR 2022 Xiaoshuai Zhang, Sai Bi, Kalyan Sunkavalli, Hao Su, Zexiang Xu

We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.

3D Reconstruction

Interactive Portrait Harmonization

no code implementations15 Mar 2022 Jeya Maria Jose Valanarasu, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Jose Echevarria, Yinglan Ma, Zijun Wei, Kalyan Sunkavalli, Vishal M. Patel

To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected \emph{region} in the reference image instead of the entire background.

Point-NeRF: Point-based Neural Radiance Fields

1 code implementation CVPR 2022 Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, Ulrich Neumann

Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field.

3D Reconstruction Neural Rendering

SSH: A Self-Supervised Framework for Image Harmonization

1 code implementation ICCV 2021 Yifan Jiang, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Kalyan Sunkavalli, Simon Chen, Sohrab Amirghodsi, Sarah Kong, Zhangyang Wang

Image harmonization aims to improve the quality of image compositing by matching the "appearance" (\eg, color tone, brightness and contrast) between foreground and background images.

Data Augmentation

OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets

no code implementations CVPR 2021 Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, YuHan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Hong-Xing Yu, Zexiang Xu, Kalyan Sunkavalli, Milos Hasan, Ravi Ramamoorthi, Manmohan Chandraker

Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.

Scene Understanding

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

1 code implementation CVPR 2021 Fanbo Xiang, Zexiang Xu, Miloš Hašan, Yannick Hold-Geoffroy, Kalyan Sunkavalli, Hao Su

We achieve this by introducing a 3D-to-2D texture mapping (or surface parameterization) network into volumetric representations.

Neural Rendering

Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments

no code implementations CVPR 2021 Zhihao Xia, Michaël Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti

We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments.

Denoising

MaterialGAN: Reflectance Capture using a Generative SVBRDF Model

no code implementations30 Sep 2020 Yu Guo, Cameron Smith, Miloš Hašan, Kalyan Sunkavalli, Shuang Zhao

We address the problem of reconstructing spatially-varying BRDFs from a small set of image measurements.

Neural Reflectance Fields for Appearance Acquisition

no code implementations9 Aug 2020 Sai Bi, Zexiang Xu, Pratul Srinivasan, Ben Mildenhall, Kalyan Sunkavalli, Miloš Hašan, Yannick Hold-Geoffroy, David Kriegman, Ravi Ramamoorthi

We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.

OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets

no code implementations25 Jul 2020 Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, YuHan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Zexiang Xu, Hong-Xing Yu, Kalyan Sunkavalli, Miloš Hašan, Ravi Ramamoorthi, Manmohan Chandraker

Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.

Multi-Task Learning Scene Understanding

State of the Art on Neural Rendering

no code implementations8 Apr 2020 Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B. Goldman, Michael Zollhöfer

Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.

Image Generation Neural Rendering +1

Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images

no code implementations CVPR 2020 Sai Bi, Zexiang Xu, Kalyan Sunkavalli, David Kriegman, Ravi Ramamoorthi

We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting.

All-Weather Deep Outdoor Lighting Estimation

no code implementations CVPR 2019 Jinsong Zhang, Kalyan Sunkavalli, Yannick Hold-Geoffroy, Sunil Hadap, Jonathan Eisenmann, Jean-François Lalonde

We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network.

Fast Spatially-Varying Indoor Lighting Estimation

no code implementations CVPR 2019 Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean-François Lalonde

We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image.

Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image

1 code implementation CVPR 2020 Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, Manmohan Chandraker

Our inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance.

Learning to Separate Multiple Illuminants in a Single Image

no code implementations CVPR 2019 Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli, Aswin C. Sankaranarayanan

We present a method to separate a single image captured under two illuminants, with different spectra, into the two images corresponding to the appearance of the scene under each individual illuminant.

Compositing-aware Image Search

no code implementations ECCV 2018 Hengshuang Zhao, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Brian Price, Jiaya Jia

We present a new image search technique that, given a background image, returns compatible foreground objects for image compositing tasks.

Image Retrieval

MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics

1 code implementation ECCV 2018 Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, Honglak Lee

Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode.

Human Dynamics motion prediction

Self-supervised Multi-view Person Association and Its Applications

no code implementations22 May 2018 Minh Vo, Ersin Yumer, Kalyan Sunkavalli, Sunil Hadap, Yaser Sheikh, Srinivasa Narasimhan

Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams.

Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image

no code implementations ECCV 2018 Zhengqin Li, Kalyan Sunkavalli, Manmohan Chandraker

We propose a material acquisition approach to recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera.

A Perceptual Measure for Deep Single Image Camera Calibration

no code implementations CVPR 2018 Yannick Hold-Geoffroy, Kalyan Sunkavalli, Jonathan Eisenmann, Matt Fisher, Emiliano Gambaretto, Sunil Hadap, Jean-François Lalonde

This network is trained using automatically generated samples from a large-scale panorama dataset, and considerably outperforms other methods, including recent deep learning-based approaches, in terms of standard L2 error.

Camera Calibration Image Retrieval

Scene Parsing with Global Context Embedding

1 code implementation ICCV 2017 Wei-Chih Hung, Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, Ming-Hsuan Yang

We present a scene parsing method that utilizes global context information based on both the parametric and non- parametric models.

Scene Parsing

Neural Face Editing with Intrinsic Image Disentangling

2 code implementations CVPR 2017 Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, Dimitris Samaras

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.

Facial Editing

Learning to Predict Indoor Illumination from a Single Image

no code implementations1 Apr 2017 Marc-André Gardner, Kalyan Sunkavalli, Ersin Yumer, Xiaohui Shen, Emiliano Gambaretto, Christian Gagné, Jean-François Lalonde

We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene.

Single-image RGB Photometric Stereo With Spatially-varying Albedo

no code implementations14 Sep 2016 Ayan Chakrabarti, Kalyan Sunkavalli

We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup---with three light directions multiplexed across different color channels in the observed RGB image.

Appearance Harmonization for Single Image Shadow Removal

no code implementations21 Mar 2016 Liqian Ma, Jue Wang, Eli Shechtman, Kalyan Sunkavalli, Shi-Min Hu

In this work we propose a fully automatic shadow region harmonization approach that improves the appearance compatibility of the de-shadowed region as typically produced by previous methods.

Image Generation Image Shadow Removal +1

PatchMatch-Based Automatic Lattice Detection for Near-Regular Textures

no code implementations ICCV 2015 Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N. Do, Eli Shechtman, Nathan Carr

In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images.

Automatic Content-Aware Color and Tone Stylization

no code implementations CVPR 2016 Joon-Young Lee, Kalyan Sunkavalli, Zhe Lin, Xiaohui Shen, In So Kweon

We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner.

Style Transfer

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