4 citations · 11 across the 19 of their papers we have counts for
9 papers · 1 filter
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…