Search Results for author:
Found 27 papers, 14 papers with code
Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer
The long-coveted task of reconstructing 3D geometry from images is still a standing problem.
MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation
Mesh-based learning is one of the popular approaches nowadays to learn shapes.
Pivotal Tuning for Latent-based Editing of Real Images
The key idea is pivotal tuning - a brief training process that preserves the editing quality of an in-domain latent region, while changing its portrayed identity and appearance.
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
To alleviate this problem, we introduce JOKR - a JOint Keypoint Representation that captures the motion common to both the source and target videos, without requiring any object prior or data collection.
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
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
In this work, we introduce this approach into the realm of encoder-based inversion.
Learned Queries for Efficient Local Attention
Vision Transformers (ViT) serve as powerful vision models.
Ranked #365 on
Image Classification
on ImageNet
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators
In addition, we propose training a semantic segmentation network along with the translation task, and to leverage this output as a loss term that improves robustness.
Stitch it in Time: GAN-Based Facial Editing of Real Videos
The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing.
Self-Conditioned Generative Adversarial Networks for Image Editing
We compare our models to a wide range of latent editing methods, and show that by alleviating the bias they achieve finer semantic control and better identity preservation through a wider range of transformations.
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
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.
Unaligned Supervision For Automatic Music Transcription in The Wild
In order to overcome data collection barriers, previous AMT approaches attempt to employ musical scores in the form of a digitized version of the same song or piece.
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes.
Human Motion Diffusion Model
In this paper, we introduce Motion Diffusion Model (MDM), a carefully adapted classifier-free diffusion-based generative model for the human motion domain.
Ranked #1 on
Motion Synthesis
on HumanAct12
OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields
A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities.
Single Motion Diffusion
We harness the power of diffusion models and present a denoising network explicitly designed for the task of learning from a single input motion.
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e. g. a specific face, and learns to map it into a word-embedding representing the concept.
Human Motion Diffusion as a Generative Prior
We evaluate the composition methods using an off-the-shelf motion diffusion model, and further compare the results to dedicated models trained for these specific tasks.
Ranked #4 on
Motion Synthesis
on InterHuman
Neural Projection Mapping Using Reflectance Fields
We introduce a high resolution spatially adaptive light source, or a projector, into a neural reflectance field that allows to both calibrate the projector and photo realistic light editing.
Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models
Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts.
Performance Conditioning for Diffusion-Based Multi-Instrument Music Synthesis
Building on state-of-the-art diffusion-based music generative models, we introduce performance conditioning - a simple tool indicating the generative model to synthesize music with style and timbre of specific instruments taken from specific performances.
OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks
Evasion Attacks (EA) are used to test the robustness of trained neural networks by distorting input data to misguide the model into incorrect classifications.
State of the Art on Diffusion Models for Visual Computing
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes.
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion
We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motion generation, using 2D diffusion models that were trained on motions obtained from in-the-wild videos.
Breathing Life Into Sketches Using Text-to-Video Priors
A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually.
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.
