472 citations · 907 across the 31 of their papers we have counts for
34 papers
Performance Conditioning for Diffusion-Based Multi-Instrument Music Synthesis
Ben Maman, Johannes Zeitler, Meinard Müller +1
Generating multi-instrument music from symbolic music representations is an important task in Music Information Retrieval (MIR). A central but still largely unsolved problem in thi…
Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models
Moab Arar, Rinon Gal, Yuval Atzmon +4
Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encode…
ROAR: Robust Adaptive Reconstruction of Shapes Using Planar Projections
Amir Barda, Yotam Erel, Yoni Kasten +1
The majority of existing large 3D shape datasets contain meshes that lend themselves extremely well to visual applications such as rendering, yet tend to be topologically invalid (…
Neural Projection Mapping Using Reflectance Fields
Yotam Erel, Daisuke Iwai, Amit H. Bermano
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 ligh…
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
Rinon Gal, Moab Arar, Yuval Atzmon +3
Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language p…
Human Motion Diffusion as a Generative Prior
Yonatan Shafir, Guy Tevet, Roy Kapon +1
Recent work has demonstrated the significant potential of denoising diffusion models for generating human motion, including text-to-motion capabilities. However, these methods are…
Cross-domain Compositing with Pretrained Diffusion Models
Roy Hachnochi, Mingrui Zhao, Nadav Orzech +4
Diffusion models have enabled high-quality, conditional image editing capabilities. We propose to expand their arsenal, and demonstrate that off-the-shelf diffusion models can be u…
Single Motion Diffusion
Sigal Raab, Inbal Leibovitch, Guy Tevet +3
Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging do…
OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields
Haim Sawdayee, Amir Vaxman, Amit H. Bermano
Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indic…
Human Motion Diffusion Model
Guy Tevet, Sigal Raab, Brian Gordon +3
Natural and expressive human motion generation is the holy grail of computer animation. It is a challenging task, due to the diversity of possible motion, human perceptual sensitiv…
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon +4
Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific uni…
Unaligned Supervision For Automatic Music Transcription in The Wild
Ben Maman, Amit H. Bermano
Multi-instrument Automatic Music Transcription (AMT), or the decoding of a musical recording into semantic musical content, is one of the holy grails of Music Information Retrieval…
MotionCLIP: Exposing Human Motion Generation to CLIP Space
Guy Tevet, Brian Gordon, Amir Hertz +2
We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent embedding that is disentangled, well behaved, and supports highly semantic textual descriptions. MotionCL…
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Amit H. Bermano, Rinon Gal, Yuval Alaluf +5
Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its rema…
Self-Conditioned Generative Adversarial Networks for Image Editing
Yunzhe Liu, Rinon Gal, Amit H. Bermano +2
Generative Adversarial Networks (GANs) are susceptible to bias, learned from either the unbalanced data, or through mode collapse. The networks focus on the core of the data distri…
CLIPasso: Semantically-Aware Object Sketching
Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo +5
Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scen…
Stitch it in Time: GAN-Based Facial Editing of Real Videos
Rotem Tzaban, Ron Mokady, Rinon Gal +2
The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their succe…
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators
Dvir Yerushalmi, Dov Danon, Amit H. Bermano
Supervision for image-to-image translation (I2I) tasks is hard to come by, but bears significant effect on the resulting quality. In this paper, we observe that for many Unsupervis…
Learned Queries for Efficient Local Attention
Moab Arar, Ariel Shamir, Amit H. Bermano
Vision Transformers (ViT) serve as powerful vision models. Unlike convolutional neural networks, which dominated vision research in previous years, vision transformers enjoy the ab…
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
Yuval Alaluf, Omer Tov, Ron Mokady +2
The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, d…
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Rinon Gal, Or Patashnik, Haggai Maron +2
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image? In other words: can an image generator be traine…
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
Ron Mokady, Rotem Tzaban, Sagie Benaim +2
The task of unsupervised motion retargeting in videos has seen substantial advancements through the use of deep neural networks. While early works concentrated on specific object p…
Pivotal Tuning for Latent-based Editing of Real Images
Daniel Roich, Ron Mokady, Amit H. Bermano +1
Recently, a surge of advanced facial editing techniques have been proposed that leverage the generative power of a pre-trained StyleGAN. To successfully edit an image this way, one…
MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation
Amir Barda, Yotam Erel, Amit H. Bermano
Mesh-based learning is one of the popular approaches nowadays to learn shapes. The most established backbone in this field is MeshCNN. In this paper, we propose infusing MeshCNN wi…