4 citations · 11 across the 19 of their papers we have counts for
19 papers
Generalized Audio-Driven Synthesis of Precise Drummer Motion
Álvaro G. Iñesta, Mattia Ryffel, Amit H. Bermano +2
Music-driven character animation enables and enhances transformative applications in entertainment and interactive education. However, synthesizing realistic drumming motion from a…
CARLoS: Retrieval via Concise Assessment Representation of LoRAs at Scale
Shahar Sarfaty, Adi Haviv, Uri Hacohen +3
The rapid proliferation of generative components, such as LoRAs, has created a vast but unstructured ecosystem. Existing discovery methods depend on unreliable user descriptions or…
Express4D: Expressive, Friendly, and Extensible 4D Facial Motion Generation Benchmark
Yaron Aloni, Rotem Shalev-Arkushin, Yonatan Shafir +3
Dynamic facial expression generation from natural language is a crucial task in Computer Graphics, with applications in Animation, Virtual Avatars, and Human-Computer Interaction.…
HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
Roey Ron, Guy Tevet, Haim Sawdayee +1
We present HOIDiNi, a text-driven diffusion framework for synthesizing realistic and plausible human-object interaction (HOI). HOI generation is extremely challenging since it indu…
Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion
Haim Sawdayee, Chuan Guo, Guy Tevet +3
Text-to-motion generative models span a wide range of 3D human actions but struggle with nuanced stylistic attributes such as a "Chicken" style. Due to the scarcity of style-specif…
AnyTop: Character Animation Diffusion with Any Topology
Inbar Gat, Sigal Raab, Guy Tevet +3
Generating motion for arbitrary skeletons is a longstanding challenge in computer graphics, remaining largely unexplored due to the scarcity of diverse datasets and the irregular n…
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
Rotem Shalev-Arkushin, Rinon Gal, Amit H. Bermano +1
Diffusion models enable high-quality and diverse visual content synthesis. However, they struggle to generate rare or unseen concepts. To address this challenge, we explore the usa…
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control
Guy Tevet, Sigal Raab, Setareh Cohan +5
Motion diffusion models and Reinforcement Learning (RL) based control for physics-based simulations have complementary strengths for human motion generation. The former is capable…
ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation
Rinon Gal, Adi Haviv, Yuval Alaluf +3
The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based app…
Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion
Adi Haviv, Shahar Sarfaty, Uri Hacohen +3
This work addresses the challenge of quantifying originality in text-to-image (T2I) generative diffusion models, with a focus on copyright originality. We begin by evaluating T2I m…
Masked Extended Attention for Zero-Shot Virtual Try-On In The Wild
Nadav Orzech, Yotam Nitzan, Ulysse Mizrahi +2
Virtual Try-On (VTON) is a highly active line of research, with increasing demand. It aims to replace a piece of garment in an image with one from another, while preserving person…
Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer
Sigal Raab, Inbar Gat, Nathan Sala +5
Given the remarkable results of motion synthesis with diffusion models, a natural question arises: how can we effectively leverage these models for motion editing? Existing diffusi…
V-LASIK: Consistent Glasses-Removal from Videos Using Synthetic Data
Rotem Shalev-Arkushin, Aharon Azulay, Tavi Halperin +3
Diffusion-based generative models have recently shown remarkable image and video editing capabilities. However, local video editing, particularly removal of small attributes like g…
LCM-Lookahead for Encoder-based Text-to-Image Personalization
Rinon Gal, Or Lichter, Elad Richardson +4
Recent advancements in diffusion models have introduced fast sampling methods that can effectively produce high-quality images in just one or a few denoising steps. Interestingly,…
Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes
Uri Hacohen, Adi Haviv, Shahar Sarfaty +4
The advent of Generative Artificial Intelligence (GenAI) models, including GitHub Copilot, OpenAI GPT, and Stable Diffusion, has revolutionized content creation, enabling non-profe…
MagicClay: Sculpting Meshes With Generative Neural Fields
Amir Barda, Vladimir G. Kim, Noam Aigerman +2
The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fu…
Breathing Life Into Sketches Using Text-to-Video Priors
Rinon Gal, Yael Vinker, Yuval Alaluf +4
A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually. An animated sketch opens another dimension to the expression of ideas and is wi…
OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks
Ofir Bar Tal, Adi Haviv, Amit H. Bermano
Evasion Attacks (EA) are used to test the robustness of trained neural networks by distorting input data to misguide the model into incorrect classifications. Creating these attack…
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion
Roy Kapon, Guy Tevet, Daniel Cohen-Or +1
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. As suc…