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Found 13 papers, 6 papers with code
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning.
Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence
However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e. g., coloring a sketch of an originally blue car given a reference green car).
Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).
Learning Representations by Contrasting Clusters While Bootstrapping Instances
Learning visual representations using large-scale unlabelled images is a holy grail for most of computer vision tasks.
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment.
Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE
In order to perform unconditional video generation, we must learn the distribution of the real-world videos.
Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime.
Reference-based Image Composition with Sketch via Structure-aware Diffusion Model
Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images.
LPMM: Intuitive Pose Control for Neural Talking-Head Model via Landmark-Parameter Morphable Model
To enable this, we introduce a landmark-parameter morphable model (LPMM), which offers control over the facial landmark domain through a set of semantic parameters.
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models
In this study, we aim to extend the capabilities of diffusion-based text-to-image (T2I) generation models by incorporating diverse modalities beyond textual description, such as sketch, box, color palette, and style embedding, within a single model.
AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image.
DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models
Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain.
SAVE: Protagonist Diversification with Structure Agnostic Video Editing
Finally, we decouple the motion from the appearance of the source video with an additional pseudo word.
