Search Results for author:
Found 27 papers, 12 papers with code
Towards Enhanced Controllability of Diffusion Models
We rely on the inductive bias of the progressive denoising process of diffusion models to encode pose/layout information in the spatial structure mask and semantic/style information in the style code.
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.
Structure-Guided Image Completion with Image-level and Object-level Semantic Discriminators
Moreover, the object-level discriminators take aligned instances as inputs to enforce the realism of individual objects.
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.
Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera
Appearance of dressed humans undergoes a complex geometric transformation induced not only by the static pose but also by its dynamics, i. e., there exists a number of cloth geometric configurations given a pose depending on the way it has moved.
CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level.
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.
Collaging Class-specific GANs for Semantic Image Synthesis
We propose a new approach for high resolution semantic image synthesis.
Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN
We present an algorithm for re-rendering a person from a single image under arbitrary poses.
Few-shot Image Generation via Cross-domain Correspondence
Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.
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.
Semantic Layout Manipulation with High-Resolution Sparse Attention
A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic.
Few-shot Image Generation with Elastic Weight Consolidation
Few-shot image generation seeks to generate more data of a given domain, with only few available training examples.
Unselfie: Translating Selfies to Neutral-pose Portraits in the Wild
Due to the ubiquity of smartphones, it is popular to take photos of one's self, or "selfies."
Modeling Artistic Workflows for Image Generation and Editing
People often create art by following an artistic workflow involving multiple stages that inform the overall design.
Swapping Autoencoder for Deep Image Manipulation
Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging.
Generative Tweening: Long-term Inbetweening of 3D Human Motions
We introduce a biomechanically constrained generative adversarial network that performs long-term inbetweening of human motions, conditioned on keyframe constraints.
AutoToon: Automatic Geometric Warping for Face Cartoon Generation
Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces.
On the Continuity of Rotation Representations in Neural Networks
Thus, widely used representations such as quaternions and Euler angles are discontinuous and difficult for neural networks to learn.
SwapNet: Garment Transfer in Single View Images
Garment transfer is a challenging task that requires (i) disentangling the features of the clothing from the body pose and shape and (ii) realistic synthesis of the garment texture on the new body.
Ranked #1 on
Virtual Try-on
on FashionIQ
(using extra training data)
PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup
This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo.
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
In this paper, we investigate deep image synthesis guided by sketch, color, and texture.
Ranked #2 on
Image Reconstruction
on Edge-to-Shoes
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces.
