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Found 24 papers, 16 papers with code
Diffusing Colors: Image Colorization with Text Guided Diffusion
To tackle these issues, we present a novel image colorization framework that utilizes image diffusion techniques with granular text prompts.
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Our quantitative analysis demonstrates that our method strikes a better balance between prompt alignment and identity consistency compared to the baseline methods, and these findings are reinforced by a user study.
Differential Diffusion: Giving Each Pixel Its Strength
While current techniques enable user control over the degree of change in an image edit, the controllability is limited to global changes over an entire edited region.
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts.
Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis
Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models.
Prediction of Scene Plausibility
Understanding the 3D world from 2D images involves more than detection and segmentation of the objects within the scene.
FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection
This poses a problem, especially in the era of social media, as synthetic videos of speaking humans can be used to spread misinformation in a convincing manner.
Taming Normalizing Flows
We propose an algorithm for taming Normalizing Flow models - changing the probability that the model will produce a specific image or image category.
Neural Font Rendering
Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature.
SpaText: Spatio-Textual Representation for Controllable Image Generation
Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based.
GEFF: Improving Any Clothes-Changing Person ReID Model using Gallery Enrichment with Face Features
Combined with the latest ReID models, our method achieves new SOTA results on the PRCC, LTCC, CCVID, LaST and VC-Clothes benchmarks and the proposed 42Street dataset.
Ranked #1 on
Person Re-Identification
on PRCC
Ham2Pose: Animating Sign Language Notation into Pose Sequences
Additionally, we offer a new distance measurement that considers missing keypoints, to measure the distance between pose sequences using DTW-MJE.
Blended Latent Diffusion
Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space.
DDNeRF: Depth Distribution Neural Radiance Fields
We present depth distribution neural radiance field (DDNeRF), a new method that significantly increases sampling efficiency along rays during training while achieving superior results for a given sampling budget.
Disentangled3D: Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Images
Our model can disentangle the geometry and appearance variations in the scene, i. e., we can independently sample from the geometry and appearance spaces of the generative model.
DeepShadow: Neural Shape from Shadow
We show that the self and cast shadows not only do not disturb 3D reconstruction, but can be used alone, as a strong learning signal, to recover the depth map and surface normals.
Blended Diffusion for Text-driven Editing of Natural Images
Natural language offers a highly intuitive interface for image editing.
text-guided-image-editing
Zero-Shot Text-to-Image Generation
GAN Cocktail: mixing GANs without dataset access
In the second stage, we merge the rooted models by averaging their weights and fine-tuning them for each specific domain, using only data generated by the original trained models.
Endless Loops: Detecting and Animating Periodic Patterns in Still Images
We present an algorithm for producing a seamless animated loop from a single image.
Iterative Text-based Editing of Talking-heads Using Neural Retargeting
We present a text-based tool for editing talking-head video that enables an iterative editing workflow.
State of the Art on Neural Rendering
Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.
Lifespan Age Transformation Synthesis
Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process.
Text-based Editing of Talking-head Video
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
Finding Distractors In Images
We propose a new computer vision task we call "distractor prediction."
