9 citations · 9 across the 10 of their papers we have counts for
11 papers
Multimodal Taxonomic Conditioning for Generative Plankton Imagery
Daniela Ivanova, Ozgu Goksu, Nicolas Pugeault
Automated plankton imaging produces severely long-tailed datasets, where the rare taxa of greatest ecological interest have too few images to train or evaluate classifiers reliably…
SceneHI: High-Resolution 3D-Consistent Scene Texturing with Controllable Illumination
Athanasios Tragakis, Marco Aversa, Daniela Ivanova +4
SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first to demonstrate that high-reso…
Promptable Animal Pose Tracking Across Species
Le Li, Daniela Ivanova, Nicolas Pugeault
Animal pose estimation and tracking is important for wildlife monitoring and conservation research, and with limited expert time for labelling automated approaches are imperative.…
Splat-Portrait: Generalizing Talking Heads with Gaussian Splatting
Tong Shi, Melonie de Almeida, Daniela Ivanova +2
Talking Head Generation aims at synthesizing natural-looking talking videos from speech and a single portrait image. Previous 3D talking head generation methods have relied on doma…
Pixel-to-4D: Camera-Controlled Image-to-Video Generation with Dynamic 3D Gaussians
Melonie de Almeida, Daniela Ivanova, Tong Shi +2
Humans excel at forecasting the future dynamics of a scene given just a single image. Video generation models that can mimic this ability are an essential component for intelligent…
ARTeFACT: Benchmarking Segmentation Models on Diverse Analogue Media Damage
Daniela Ivanova, Marco Aversa, Paul Henderson +1
Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While…
Unsupervised Segmentation by Diffusing, Walking and Cutting
Daniela Ivanova, Marco Aversa, Paul Henderson +1
We propose an unsupervised image segmentation method using features from pre-trained text-to-image diffusion models. Inspired by classic spectral clustering approaches, we construc…
State-of-the-Art Fails in the Art of Damage Detection
Daniela Ivanova, Marco Aversa, Paul Henderson +1
Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While…
Sampling 3D Gaussian Scenes in Seconds with Latent Diffusion Models
Paul Henderson, Melonie de Almeida, Daniela Ivanova +1
We present a latent diffusion model over 3D scenes, that can be trained using only 2D image data. To achieve this, we first design an autoencoder that maps multi-view images to 3D…
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
Marco Aversa, Gabriel Nobis, Miriam Hägele +10
We present DiffInfinite, a hierarchical diffusion model that generates arbitrarily large histological images while preserving long-range correlation structural information. Our app…
Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans
Daniela Ivanova, John Williamson, Paul Henderson
Digital scans of analogue photographic film typically contain artefacts such as dust and scratches. Automated removal of these is an important part of preservation and disseminatio…