46 citations · 186 across the 19 of their papers we have counts for
22 papers
INRFlow: Flow Matching for INRs in Ambient Space
Yuyang Wang, Anurag Ranjan, Josh Susskind +1
Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or…
Pointersect: Neural Rendering with Cloud-Ray Intersection
Jen-Hao Rick Chang, Wei-Yu Chen, Anurag Ranjan +2
We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capabili…
FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction
Noah Stier, Anurag Ranjan, Alex Colburn +4
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…
FaceLit: Neural 3D Relightable Faces
Anurag Ranjan, Kwang Moo Yi, Jen-Hao Rick Chang +1
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 image…
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu +2
The recent amalgamation of transformer and convolutional designs has led to steady improvements in accuracy and efficiency of the models. In this work, we introduce FastViT, a hybr…
Naturalistic Head Motion Generation from Speech
Trisha Mittal, Zakaria Aldeneh, Masha Fedzechkina +2
Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for providing a rich interactive experience. Most prior works assess the qual…
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
Chien-Yu Lin, Anish Prabhu, Thomas Merth +4
Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convol…
MobileOne: An Improved One millisecond Mobile Backbone
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu +2
Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency o…
NeuMan: Neural Human Radiance Field from a Single Video
Wei Jiang, Kwang Moo Yi, Golnoosh Samei +2
Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences. We propose a novel framework to reconstruct the human and the scene that ca…
Token Pooling in Vision Transformers
Dmitrii Marin, Jen-Hao Rick Chang, Anurag Ranjan +3
Despite the recent success in many applications, the high computational requirements of vision transformers limit their use in resource-constrained settings. While many existing me…
LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference Time
Elvis Nunez, Maxwell Horton, Anish Prabhu +3
When deploying deep learning models to a device, it is traditionally assumed that available computational resources (compute, memory, and power) remain static. However, real-world…
MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias
Nataniel Ruiz, Barry-John Theobald, Anurag Ranjan +2
To detect bias in face recognition networks, it can be useful to probe a network under test using samples in which only specific attributes vary in some controlled way. However, ca…
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
Mike Roberts, Jason Ramapuram, Anurag Ranjan +5
For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We address this challenge by introducing Hyp…
GIF: Generative Interpretable Faces
Partha Ghosh, Pravir Singh Gupta, Roy Uziel +3
Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unreali…
Attacking Optical Flow
Anurag Ranjan, Joel Janai, Andreas Geiger +1
Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-drivin…
Learning Multi-Human Optical Flow
Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas +3
The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However…
Learning to Dress 3D People in Generative Clothing
Qianli Ma, Jinlong Yang, Anurag Ranjan +4
Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not…
Capture, Learning, and Synthesis of 3D Speaking Styles
Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw +2
Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, mode…
Generating 3D faces using Convolutional Mesh Autoencoders
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal +1
Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as…
Learning Human Optical Flow
Anurag Ranjan, Javier Romero, Michael J. Black
The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that…
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Anurag Ranjan, Varun Jampani, Lukas Balles +4
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation…
Optical Flow Estimation using a Spatial Pyramid Network
Anurag Ranjan, Michael J. Black
We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one i…