7 code implementations • CVPR 2023 • Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan
Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device.
Ranked #586 on Image Classification on ImageNet
4 code implementations • ICCV 2023 • Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan
To this end, we introduce a novel token mixing operator, RepMixer, a building block of FastViT, that uses structural reparameterization to lower the memory access cost by removing skip-connections in the network.
2 code implementations • ICCV 2021 • Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, Joshua M. Susskind
To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77, 400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry.
1 code implementation • 23 Mar 2022 • Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, Anurag Ranjan
Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences.
1 code implementation • CVPR 2019 • Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw, Anurag Ranjan, Michael J. Black
To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers.
8 code implementations • CVPR 2017 • Anurag Ranjan, Michael J. Black
We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning.
Ranked #8 on Dense Pixel Correspondence Estimation on HPatches
Dense Pixel Correspondence Estimation Optical Flow Estimation
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.
Ranked #66 on Monocular Depth Estimation on KITTI Eigen split
2 code implementations • ECCV 2018 • Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black
To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface.
Ranked #4 on Face Alignment on FaceScape
1 code implementation • 31 Aug 2020 • Partha Ghosh, Pravir Singh Gupta, Roy Uziel, Anurag Ranjan, Michael Black, Timo Bolkart
Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
1 code implementation • CVPR 2020 • Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, Michael J. Black
To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.
1 code implementation • ICCV 2023 • Noah Stier, Anurag Ranjan, Alex Colburn, Yajie Yan, Liang Yang, Fangchang Ma, Baptiste Angles
Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency.
1 code implementation • 14 Jun 2018 • Anurag Ranjan, Javier Romero, Michael J. Black
Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods.
1 code implementation • ICCV 2019 • Anurag Ranjan, Joel Janai, Andreas Geiger, Michael J. Black
In this paper, we extend adversarial patch attacks to optical flow networks and show that such attacks can compromise their performance.
2 code implementations • 24 Oct 2019 • Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas, Siyu Tang, Javier Romero, Michael J. Black
Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset.
1 code implementation • 23 Oct 2023 • Byeongjoo Ahn, Karren Yang, Brian Hamilton, Jonathan Sheaffer, Anurag Ranjan, Miguel Sarabia, Oncel Tuzel, Jen-Hao Rick Chang
Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sources, we estimate the sound anywhere in the scene.
1 code implementation • 29 Nov 2023 • Muhammed Kocabas, Jen-Hao Rick Chang, James Gabriel, Oncel Tuzel, Anurag Ranjan
We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being ~100x faster to train over previous work.
1 code implementation • 21 Jul 2022 • Chien-Yu Lin, Anish Prabhu, Thomas Merth, Sachin Mehta, Anurag Ranjan, Maxwell Horton, Mohammad Rastegari
In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN).
1 code implementation • 8 Oct 2021 • Elvis Nunez, Maxwell Horton, Anish Prabhu, Anurag Ranjan, Ali Farhadi, Mohammad Rastegari
Our models require no retraining, thus our subspace of models can be deployed entirely on-device to allow adaptive network compression at inference time.
no code implementations • ECCV 2018 • Joel Janai, Fatma Guney, Anurag Ranjan, Michael Black, Andreas Geiger
In this paper, we propose a framework for unsupervised learning of optical flow and occlusions over multiple frames.
no code implementations • 9 Dec 2020 • Nataniel Ruiz, Barry-John Theobald, Anurag Ranjan, Ahmed Hussein Abdelaziz, Nicholas Apostoloff
Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression.
no code implementations • 8 Oct 2021 • Dmitrii Marin, Jen-Hao Rick Chang, Anurag Ranjan, Anish Prabhu, Mohammad Rastegari, Oncel Tuzel
Token Pooling is a simple and effective operator that can benefit many architectures.
no code implementations • 26 Oct 2022 • Trisha Mittal, Zakaria Aldeneh, Masha Fedzechkina, Anurag Ranjan, Barry-John Theobald
Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for providing a rich interactive experience.
no code implementations • CVPR 2023 • Anurag Ranjan, Kwang Moo Yi, Jen-Hao Rick Chang, Oncel Tuzel
We propose a generative framework, FaceLit, capable of generating a 3D face that can be rendered at various user-defined lighting conditions and views, learned purely from 2D images in-the-wild without any manual annotation.
no code implementations • CVPR 2023 • Jen-Hao Rick Chang, Wei-Yu Chen, Anurag Ranjan, Kwang Moo Yi, Oncel Tuzel
Specifically, we train a set transformer that, given a small number of local neighbor points along a light ray, provides the intersection point, the surface normal, and the material blending weights, which are used to render the outcome of this light ray.
no code implementations • 30 Nov 2023 • Karren D. Yang, Anurag Ranjan, Jen-Hao Rick Chang, Raviteja Vemulapalli, Oncel Tuzel
While these models can achieve high-quality lip articulation for speakers in the training set, they are unable to capture the full and diverse distribution of 3D facial motions that accompany speech in the real world.
no code implementations • 15 Dec 2023 • Chien-Yu Lin, Qichen Fu, Thomas Merth, Karren Yang, Anurag Ranjan
Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook.