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Found 19 papers, 12 papers with code
PALP: Prompt Aligned Personalization of Text-to-Image Models
We term our approach prompt-aligned personalization.
Style Aligned Image Generation via Shared Attention
Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts.
AnyLens: A Generative Diffusion Model with Any Rendering Lens
State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth.
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.
SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling
We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature.
Delta Denoising Score
We introduce Delta Denoising Score (DDS), a novel scoring function for text-based image editing that guides minimal modifications of an input image towards the content described in a target prompt.
Word-As-Image for Semantic Typography
A word-as-image is a semantic typography technique where a word illustration presents a visualization of the meaning of the word, while also preserving its readability.
Null-text Inversion for Editing Real Images using Guided Diffusion Models
Our Null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and prompt editing, showing high-fidelity editing of real images.
Ranked #4 on
Text-based Image Editing
on PIE-Bench
Prompt-to-Prompt Image Editing with Cross Attention Control
Editing is challenging for these generative models, since an innate property of an editing technique is to preserve most of the original image, while in the text-based models, even a small modification of the text prompt often leads to a completely different outcome.
Ranked #14 on
Text-based Image Editing
on PIE-Bench
MotionCLIP: Exposing Human Motion Generation to CLIP Space
MotionCLIP gains its unique power by aligning its latent space with that of the Contrastive Language-Image Pre-training (CLIP) model.
SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation
Neural implicit fields are quickly emerging as an attractive representation for learning based techniques.
ClipCap: CLIP Prefix for Image Captioning
Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image.
Ranked #1 on
Image Captioning
on Conceptual Captions
Mesh Draping: Parametrization-Free Neural Mesh Transfer
The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh.
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.
SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands.
Deep Geometric Texture Synthesis
Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus.
PointGMM: a Neural GMM Network for Point Clouds
We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud.
Blind Visual Motif Removal from a Single Image
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image.
MeshCNN: A Network with an Edge
In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes.
