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Found 35 papers, 13 papers with code
Generative Portrait Shadow Removal
For robust and natural shadow removal, we propose to train the diffusion model with a compositional repurposing framework: a pre-trained text-guided image generation model is first fine-tuned to harmonize the lighting and color of the foreground with a background scene by using a background harmonization dataset; and then the model is further fine-tuned to generate a shadow-free portrait image via a shadow-paired dataset.
Removing Distributional Discrepancies in Captions Improves Image-Text Alignment
In this paper, we introduce a model designed to improve the prediction of image-text alignment, targeting the challenge of compositional understanding in current visual-language models.
GroupDiff: Diffusion-based Group Portrait Editing
The training data engine covers the diverse needs of group portrait editing.
ActAnywhere: Subject-Aware Video Background Generation
Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community.
UniHuman: A Unified Model for Editing Human Images in the Wild
In this paper, we propose UniHuman, a unified model that addresses multiple facets of human image editing in real-world settings.
Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models
Despite recent significant strides achieved by diffusion-based Text-to-Image (T2I) models, current systems are still less capable of ensuring decent compositional generation aligned with text prompts, particularly for the multi-object generation.
Consistent Multimodal Generation via A Unified GAN Framework
We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model.
Putting People in Their Place: Affordance-Aware Human Insertion into Scenes
Given a scene image with a marked region and an image of a person, we insert the person into the scene while respecting the scene affordances.
Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods
Second, we propose timestep-dependent weight scheduling for content and style features to further improve the performance.
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis
We show that MCM enables user control over the spatial layout of the image and leads to increased control over the image generation process.
Zero-shot Image-to-Image Translation
However, it is still challenging to directly apply these models for editing real images for two reasons.
Ranked #13 on
Text-based Image Editing
on PIE-Bench
Complete 3D Human Reconstruction From a Single Incomplete Image
This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e. g., a torso.
UMFuse: Unified Multi View Fusion for Human Editing applications
Numerous pose-guided human editing methods have been explored by the vision community due to their extensive practical applications.
VGFlow: Visibility guided Flow Network for Human Reposing
The task of human reposing involves generating a realistic image of a person standing in an arbitrary conceivable pose.
Contrastive Learning for Diverse Disentangled Foreground Generation
We introduce a new method for diverse foreground generation with explicit control over various factors.
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing
We propose a new method to invert and edit such complex images in the latent space of GANs, such as StyleGAN2.
GIRAFFE HD: A High-Resolution 3D-aware Generative Model
3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation.
InsetGAN for Full-Body Image Generation
Instead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e. g., human body) and a set of specialized GANs, or insets, focus on different parts (e. g., faces, shoes) that can be seamlessly inserted onto the global canvas.
Dance In the Wild: Monocular Human Animation with Neural Dynamic Appearance Synthesis
Synthesizing dynamic appearances of humans in motion plays a central role in applications such as AR/VR and video editing.
Collaging Class-specific GANs for Semantic Image Synthesis
We propose a new approach for high resolution semantic image synthesis.
IMAGINE: Image Synthesis by Image-Guided Model Inversion
We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample.
Generating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e. g., dogs and cars).
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e. g., dogs and cars).
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Our key idea is to decorrelate feature representations of a category from its co-occurring context.
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
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, for mix-and-match image generation.
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data
We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data.
You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection
We use the W-RPN to generate high precision object proposals, which are in turn used to re-rank high recall proposals like edge boxes or selective search according to their spatial overlap.
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.
Ranked #1 on
Image Clustering
on Stanford Dogs
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
Our approach only needs to modify the input image and can work with any network to improve its performance.
DOCK: Detecting Objects by transferring Common-sense Knowledge
We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories.
Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest.
Identifying First-person Camera Wearers in Third-person Videos
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 wearing body-worn cameras while a third-person static camera also captures the scene.
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization
We propose `Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos.
Ranked #24 on
Weakly Supervised Action Localization
on THUMOS 2014
End-to-End Localization and Ranking for Relative Attributes
We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons.
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
The status quo approach to training object detectors requires expensive bounding box annotations.
