Search Results for author:
Found 10 papers, 4 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.
Cooperative Distribution Alignment via JSD Upper Bound
Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned distribution given only samples from each distribution.
Why be adversarial? Let's cooperate!: Cooperative Dataset Alignment via JSD Upper Bound
Unsupervised dataset alignment estimates a transformation that maps two or more source domains to a shared aligned domain given only the domain datasets.
StyleUV: Diverse and High-fidelity UV Map Generative Model
Meanwhile, with the advent of Generative Adversarial Networks (GANs), there has been great progress in reconstructing realistic 2D images.
Enhanced 3DMM Attribute Control via Synthetic Dataset Creation Pipeline
While facial attribute manipulation of 2D images via Generative Adversarial Networks (GANs) has become common in computer vision and graphics due to its many practical uses, research on 3D attribute manipulation is relatively undeveloped.
Unpaired Image Translation via Adaptive Convolution-based Normalization
Disentangling content and style information of an image has played an important role in recent success in image translation.
What and Where to Translate: Local Mask-based Image-to-Image Translation
First, those methods extract style from an entire exemplar which includes noisy information, which impedes a translation model from properly extracting the intended style of the exemplar.
Local Image-to-Image Translation via Pixel-wise Highway Adaptive Instance Normalization
Recently, image-to-image translation has seen a significant success.
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation.
Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette.
