Image & Video AI

arXiv:2606.31683v1 Announce Type: cross Abstract: Diffusion models have emerged as a dominant paradigm in generative modeling, enabling high-fidelity sampling f

arXiv:2606.29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled. This occurs when engines up

arXiv:2606.29453v1 Announce Type: cross Abstract: Arbitrary-Scale Super-Resolution (ASR) reconstructs images at continuous magnification factors. Recent methods

arXiv:2606.27923v1 Announce Type: cross Abstract: We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a sin

arXiv:2606.28112v1 Announce Type: cross Abstract: Degradation-aware prompts, conditions, and latent priors are increasingly used in image restoration, yet they

arXiv:2411.19537v2 Announce Type: replace-cross Abstract: We survey deepfake generation and detection techniques, covering all deepfake media types: image, vide

arXiv:2606.26947v1 Announce Type: cross Abstract: While personalized image generation has achieved remarkable progress, multi-reference image generation (MRIG)

arXiv:2606.27147v1 Announce Type: cross Abstract: Unlike diffusion-based models that operate in continuous latent spaces, autoregressive unified multimodal mode

arXiv:2606.20909v1 Announce Type: cross Abstract: Earth observation imagery plays a critical role in environmental monitoring, urban planning, disaster assessme

arXiv:2606.20991v1 Announce Type: cross Abstract: Text-to-image generation is a form of generative artificial intelligence (GenAI) that converts textual descrip

arXiv:2606.21033v1 Announce Type: cross Abstract: Image compression for machines calls for a unified codec that serves multiple downstream vision tasks. Existin

arXiv:2606.23679v1 Announce Type: cross Abstract: Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence come

arXiv:2602.03448v2 Announce Type: replace-cross Abstract: Multi-subject image generation aims to synthesize images that faithfully preserve the identities of mu

arXiv:2606.17646v1 Announce Type: cross Abstract: Saliency map visualizations explain image-based AI predictions by pointing to regions, but these are often uni

arXiv:2606.16996v1 Announce Type: cross Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but appl

arXiv:2411.05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. Howeve

arXiv:2505.11034v2 Announce Type: replace-cross Abstract: Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synt

arXiv:2606.08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs rema

arXiv:2606.07558v1 Announce Type: cross Abstract: Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents,

arXiv:2511.18676v2 Announce Type: replace-cross Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answ

I run evaluations on generative image models as part of my workflow, mostly comparing coherence, prompt adherence, and compositional accuracy across different a

arXiv:2606.07036v1 Announce Type: cross Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including

arXiv:2606.05552v1 Announce Type: cross Abstract: Despite progress in image tokenization, standard methods encode redundant information by mixing all granularit

arXiv:2606.00094v1 Announce Type: cross Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires lear

arXiv:2606.02453v1 Announce Type: cross Abstract: Despite the remarkable fidelity of generative models, they frequently suffer from mode collapse. Existing stra

arXiv:2605.30341v1 Announce Type: cross Abstract: Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We i

arXiv:2506.07813v2 Announce Type: replace-cross Abstract: Arbitrary-scale image super-resolution aims to upsample images to any desired resolution, offering gre

arXiv:2605.24621v1 Announce Type: cross Abstract: Scattering transforms achieve Lipschitz stability and translation invariance, but dense prediction tasks requi

arXiv:2605.24769v1 Announce Type: cross Abstract: Hyperspectral image restoration faces several challenges, including limited training data, strong sensor speci

arXiv:2412.07333v2 Announce Type: replace-cross Abstract: Pose-Guided Person Image Synthesis (PGPIS) aims to generate human images in specified poses while pres

arXiv:2604.08213v2 Announce Type: replace-cross Abstract: High-quality source-target image pairs with precise editing instructions are essential for instruction

arXiv:2605.23264v1 Announce Type: cross Abstract: Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limi

arXiv:2602.00122v2 Announce Type: replace-cross Abstract: In recent years, image editing models have made significant progress, enabling users to manipulate vis

arXiv:2602.11553v2 Announce Type: replace-cross Abstract: Image denoising aims to remove noise while preserving structural details and perceptual realism, yet d

arXiv:2605.15880v1 Announce Type: cross Abstract: Thermal infrared imaging is robust to illumination variations and smoke interference, making it important for

arXiv:2605.08282v1 Announce Type: cross Abstract: Purpose: We aim to enhance the image quality of point-of-care ultrasound (POCUS) devices using deep learning a

arXiv:2605.09296v1 Announce Type: cross Abstract: Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing

arXiv:2605.07230v1 Announce Type: cross Abstract: Autoregressive generation is a powerful approach for high-fidelity image synthesis, but it remains computation

arXiv:2605.05627v1 Announce Type: cross Abstract: Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys ar

arXiv:2605.05889v1 Announce Type: cross Abstract: Diffusion-based image-to-image (I2I) translation excels in high-fidelity generation but suffers from slow samp

arXiv:2605.06127v1 Announce Type: cross Abstract: Real-world image degradation is often unknown, spatially non-uniform, and compositional, requiring all-in-one

arXiv:2602.02493v2 Announce Type: replace-cross Abstract: Pixel diffusion generates images directly in pixel space, avoiding the VAE artifacts and representatio

arXiv:2605.04503v1 Announce Type: cross Abstract: Image Difference Captioning (IDC) generates natural language descriptions that precisely identify differences

arXiv:2605.00883v1 Announce Type: cross Abstract: Face swapping has witnessed significant progress in recent years, largely driven by advances in deep generativ

arXiv:2605.02767v1 Announce Type: cross Abstract: Diffusion models have recently demonstrated strong performance for image restoration tasks, including super-re

arXiv:2503.15984v3 Announce Type: replace-cross Abstract: Modern image restoration and super-resolution methods utilize deep learning due to its superior perfor

arXiv:2411.18929v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models learn to remove noise added during training, generating novel data (e.g

arXiv:2604.23325v1 Announce Type: cross Abstract: Emotionally talking head video generation aims to generate expressive portrait videos with accurate lip synchr