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Alex Levinshtein

7 accepted papers

2026

Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration

Amirhossein Kazerouni, Maitreya Suin, Tristan Aumentado-Armstrong, Sina Honari, Amanpreet Walia, Iqbal Mohomed +3
CVPR 2026

Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods focus solely on facial regions, neglecting degradation across the full scene, including body and background, which limi

Cited by 0Source ↗Scholar ↗
2026

RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution

Ali Mosleh, Faraz Ali, Fengjia Zhang, Stavros Tsogkas, Junyong Lee, Michael S. Brown +1
CVPR 2026

Digital zoom on smartphones relies on learning-based super-resolution (SR) models that operate on RAW sensor images, but obtaining sensor-specific training data is challenging due to the lack of ground-truth images. Synthetic data generation via "unprocessing" pipelines offers a potential solution b

Cited by 0Source ↗Scholar ↗
2025

Augmenting Perceptual Super-Resolution via Image Quality Predictors

Fengjia Zhang, Samrudhdhi B. Rangrej, Tristan Aumentado-Armstrong, Afsaneh Fazly, Alex Levinshtein
CVPR 2025poster

Super-resolution (SR), a classical inverse problem in computer vision, is inherently ill-posed, inducing a distribution of plausible solutions for every input. However, the desired result is not simply the expectation of this distribution, which is the blurry image obtained by minimizing pixelwise e…

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2024

Watch Your Steps: Local Image and Scene Editing by Text Instructions

Ashkan Mirzaei*, Tristan T Aumentado-Armstrong, Marcus A Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G Derpanis +1
ECCV 2024oral

"The success of denoising diffusion models in generating and editing images has sparked interest in using diffusion models for editing 3D scenes represented via neural radiance fields (NeRFs). However, current 3D editing methods lack a way to both pinpoint the edit location and limit changes to the…

Cited by 35Source ↗PDF ↗Scholar ↗
2023

Reference-guided Controllable Inpainting of Neural Radiance Fields

Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G. Derpanis +1
ICCV 2023poster

The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools. Here, we focus on inpainting regions in a view-consistent and controllable manner. In addition to the typical NeRF inputs and masks delineating the unwanted region in each view, we require…

Cited by 42PDF ↗code ↗Scholar ↗
2023

SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting With Neural Radiance Fields

Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis, Jonathan Kelly, Marcus A. Brubaker, Igor Gilitschenski +1
CVPR 2023poster

Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene…

Cited by 140Source ↗PDF ↗code ↗Scholar ↗
2022

Day-to-Night Image Synthesis for Training Nighttime Neural ISPs

Abhijith Punnappurath, Abdullah Abuolaim, Abdelrahman Abdelhamed, Alex Levinshtein, Michael S. Brown
CVPR 2022oral

Many flagship smartphone cameras now use a dedicated neural image signal processor (ISP) to render noisy raw sensor images to the final processed output. Training nightmode ISP networks relies on large-scale datasets of image pairs with: (1) a noisy raw image captured with a short exposure and a hig…

Cited by 35PDF ↗code ↗Scholar ↗