Search Results for author:
Found 15 papers, 10 papers with code
LCM-Lookahead for Encoder-based Text-to-Image Personalization
In this work, we explore the potential of using such shortcut-mechanisms to guide the personalization of text-to-image models to specific facial identities.
MyVLM: Personalizing VLMs for User-Specific Queries
To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image.
ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints
Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery.
A Neural Space-Time Representation for Text-to-Image Personalization
We observe that one can significantly improve the convergence and visual fidelity of the concept by introducing a textual bypass, where our neural mapper additionally outputs a residual that is added to the output of the text encoder.
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
We show that using proxies allows a wide variety of editing options, such as adjusting the placement of each independent object, removing objects from a scene, or refining an object.
TEXTure: Text-Guided Texturing of 3D Shapes
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes.
NeRN -- Learning Neural Representations for Neural Networks
Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos.
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
This unique combination of text and shape guidance allows for increased control over the generation process.
Ranked #3 on
Text to 3D
on T$^3$Bench
Rethinking FUN: Frequency-Domain Utilization Networks
The search for efficient neural network architectures has gained much focus in recent years, where modern architectures focus not only on accuracy but also on inference time and model size.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
It's All About The Scale -- Efficient Text Detection Using Adaptive Scaling
If we were to have a prior telling us the coarse location of text instances in the image and their approximate scale, we could have adaptively chosen which regions to process and how to rescale them, thus significantly reducing the processing time.
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
It has been recently shown that neural networks can recover the geometric structure of a face from a single given image.
Learning Detailed Face Reconstruction from a Single Image
In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image.
3D Face Reconstruction by Learning from Synthetic Data
Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications.
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
SEBOOST applies a secondary optimization process in the subspace spanned by the last steps and descent directions.
