67 citations · 126 across the 21 of their papers we have counts for
23 papers
Consistent Multimodal Generation via A Unified GAN Framework
Zhen Zhu, Yijun Li, Weijie Lyu +4
We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model. The challenge is to produce outputs that are reali…
Putting People in Their Place: Affordance-Aware Human Insertion into Scenes
Sumith Kulal, Tim Brooks, Alex Aiken +5
We study the problem of inferring scene affordances by presenting a method for realistically inserting people into scenes. Given a scene image with a marked region and an image of…
Zero-shot Image-to-Image Translation
Gaurav Parmar, Krishna Kumar Singh, Richard Zhang +3
Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply th…
Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods
Wonwoong Cho, Hareesh Ravi, Midhun Harikumar +5
As Diffusion Models have shown promising performance, a lot of efforts have been made to improve the controllability of Diffusion Models. However, how to train Diffusion Models to…
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis
Cusuh Ham, James Hays, Jingwan Lu +3
We present multimodal conditioning modules (MCM) for enabling conditional image synthesis using pretrained diffusion models. Previous multimodal synthesis works rely on training ne…
Contrastive Learning for Diverse Disentangled Foreground Generation
Yuheng Li, Yijun Li, Jingwan Lu +3
We introduce a new method for diverse foreground generation with explicit control over various factors. Existing image inpainting based foreground generation methods often struggle…
UMFuse: Unified Multi View Fusion for Human Editing applications
Rishabh Jain, Mayur Hemani, Duygu Ceylan +4
Numerous pose-guided human editing methods have been explored by the vision community due to their extensive practical applications. However, most of these methods still use an ima…
VGFlow: Visibility guided Flow Network for Human Reposing
Rishabh Jain, Krishna Kumar Singh, Mayur Hemani +4
The task of human reposing involves generating a realistic image of a person standing in an arbitrary conceivable pose. There are multiple difficulties in generating perceptually a…
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing
Gaurav Parmar, Yijun Li, Jingwan Lu +3
Existing GAN inversion and editing methods work well for aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categori…
GIRAFFE HD: A High-Resolution 3D-aware Generative Model
Yang Xue, Yuheng Li, Krishna Kumar Singh +1
3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRA…
InsetGAN for Full-Body Image Generation
Anna Frühstück, Krishna Kumar Singh, Eli Shechtman +3
While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities…
Dance In the Wild: Monocular Human Animation with Neural Dynamic Appearance Synthesis
Tuanfeng Y. Wang, Duygu Ceylan, Krishna Kumar Singh +1
Synthesizing dynamic appearances of humans in motion plays a central role in applications such as AR/VR and video editing. While many recent methods have been proposed to tackle th…
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman +3
Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. However, strongly relying on context risks a model's generalizabili…
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
Yuheng Li, Krishna Kumar Singh, Utkarsh Ojha +1
We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, fo…
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data
Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh +1
We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data. We first investigate the issues s…
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
Krishna Kumar Singh, Hao Yu, Aron Sarmasi +2
We propose 'Hide-and-Seek' a general purpose data augmentation technique, which is complementary to existing data augmentation techniques and is beneficial for various visual recog…
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained obj…
DOCK: Detecting Objects by transferring Common-sense Knowledge
Krishna Kumar Singh, Santosh Divvala, Ali Farhadi +1
We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories. In our setting, the training data for the sourc…
Identifying First-person Camera Wearers in Third-person Videos
Chenyou Fan, Jangwon Lee, Mingze Xu +4
We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wea…
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization
Krishna Kumar Singh, Yong Jae Lee
We propose `Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised meth…
End-to-End Localization and Ranking for Relative Attributes
Krishna Kumar Singh, Yong Jae Lee
We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike…
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
Krishna Kumar Singh, Fanyi Xiao, Yong Jae Lee
The status quo approach to training object detectors requires expensive bounding box annotations. Our framework takes a markedly different direction: we transfer tracked object box…
CPU and/or GPU: Revisiting the GPU Vs. CPU Myth
Kishore Kothapalli, Dip Sankar Banerjee, P. J. Narayanan +13
Parallel computing using accelerators has gained widespread research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth seve…