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Tristan Aumentado-Armstrong

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

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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

PolyOculus: Simultaneous Multi-view Image-based Novel View Synthesis

Jason J. Yu*, Tristan Aumentado-Armstrong, Fereshteh Forghani, Konstantinos G. Derpanis, Marcus A. Brubaker
ECCV 2024poster

"This paper considers the problem of generative novel view synthesis (GNVS), generating novel, plausible views of a scene given a limited number of known views. Here, we propose a set-based generative model that can simultaneously generate multiple, self-consistent new views, conditioned on any numb…

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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

Representing 3D Shapes With Probabilistic Directed Distance Fields

Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan D. Jepson
CVPR 2022poster

Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks. Yet, explicit shape representations (e.g., voxels, point clouds, meshes), while relatively easily rendered, often suffer…

Cited by 24PDF ↗Scholar ↗
2019

Geometric Disentanglement for Generative Latent Shape Models

Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, Sven Dickinson
ICCV 2019poster

Representing 3D shapes is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. However, it remains an open problem to learn a…

Cited by 59PDF ↗Scholar ↗