1 citations · 1 across the 4 of their papers we have counts for
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FlowBender: Feedback-Aware Training for Self-Correcting Conditional Flows
Daniel Gilo, Sven Elflein, Ido Sobol +1
Conditional diffusion and flow models routinely fail to satisfy the very constraints that define their task. For instance, a depth-conditioned model often produces images whose re-…
RealMaster: Lifting Rendered Scenes into Photorealistic Video
Dana Cohen-Bar, Ido Sobol, Raphael Bensadoun +5
State-of-the-art video generation models produce remarkable photorealism, but they lack the precise control required to align generated content with specific scene requirements. Fu…
Appreciate the View: A Task-Aware Evaluation Framework for Novel View Synthesis
Saar Stern, Ido Sobol, Or Litany
The goal of Novel View Synthesis (NVS) is to generate realistic images of a given content from unseen viewpoints. But how can we trust that a generated image truly reflects the int…
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision
Chensheng Peng, Ido Sobol, Masayoshi Tomizuka +3
We present a novel framework for training 3D image-conditioned diffusion models using only 2D supervision. Recovering 3D structure from 2D images is inherently ill-posed due to the…
Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering
Ido Sobol, Chenfeng Xu, Or Litany
Generating realistic images from arbitrary views based on a single source image remains a significant challenge in computer vision, with broad applications ranging from e-commerce…