2 citations · 9 across the 15 of their papers we have counts for
17 papers
Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising
Mohammad Mohammadi, Sina Honari, Stavros Tsogkas +6
Raw images inherently suffer from noise due to the stochastic nature of light and sensor hardware imperfections. As real photon counts fall, the ratio of this noise to the signal d…
Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with Progressive Texture Infilling
Shuhong Zheng, Ashkan Mirzaei, Igor Gilitschenski
Current 3D/4D generation methods are usually optimized for photorealism, efficiency, and aesthetics. However, they often fail to preserve the semantic identity of the subject acros…
LuxDiT: Lighting Estimation with Video Diffusion Transformer
Ruofan Liang, Kai He, Zan Gojcic +4
Estimating scene lighting from a single image or video remains a longstanding challenge in computer vision and graphics. Learning-based approaches are constrained by the scarcity o…
VibES: Induced Vibration for Persistent Event-Based Sensing
Vincenzo Polizzi, Stephen Yang, Quentin Clark +3
Event cameras are a bio-inspired class of sensors that asynchronously measure per-pixel intensity changes. Under fixed illumination conditions in static or low-motion scenes, rigid…
UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting
Kai He, Ruofan Liang, Jacob Munkberg +7
We address the challenge of relighting a single image or video, a task that demands precise scene intrinsic understanding and high-quality light transport synthesis. Existing end-t…
CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion
Kai He, Chin-Hsuan Wu, Igor Gilitschenski
Recent advances in 3D representations, such as Neural Radiance Fields and 3D Gaussian Splatting, have greatly improved realistic scene modeling and novel-view synthesis. However, a…
EventSplat: 3D Gaussian Splatting from Moving Event Cameras for Real-time Rendering
Toshiya Yura, Ashkan Mirzaei, Igor Gilitschenski
We introduce a method for using event camera data in novel view synthesis via Gaussian Splatting. Event cameras offer exceptional temporal resolution and a high dynamic range. Leve…
Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
Hanxue Liang, Jiawei Ren, Ashkan Mirzaei +8
Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle wi…
Towards Unsupervised Blind Face Restoration using Diffusion Prior
Tianshu Kuai, Sina Honari, Igor Gilitschenski +1
Blind face restoration methods have shown remarkable performance, particularly when trained on large-scale synthetic datasets with supervised learning. These datasets are often gen…
GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting
Umangi Jain, Ashkan Mirzaei, Igor Gilitschenski
We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented b…
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
Ashkan Mirzaei, Riccardo De Lutio, Seung Wook Kim +5
Neural reconstruction approaches are rapidly emerging as the preferred representation for 3D scenes, but their limited editability is still posing a challenge. In this work, we pro…
Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations
Tristan Aumentado-Armstrong, Ashkan Mirzaei, Marcus A. Brubaker +4
Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graph…
Watch Your Steps: Local Image and Scene Editing by Text Instructions
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4
Denoising diffusion models have enabled high-quality image generation and editing. We present a method to localize the desired edit region implicit in a text instruction. We levera…
Reference-guided Controllable Inpainting of Neural Radiance Fields
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4
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 contro…
SparsePose: Sparse-View Camera Pose Regression and Refinement
Samarth Sinha, Jason Y. Zhang, Andrea Tagliasacchi +2
Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation…
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis +4
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 editi…
LaTeRF: Label and Text Driven Object Radiance Fields
Ashkan Mirzaei, Yash Kant, Jonathan Kelly +1
Obtaining 3D object representations is important for creating photo-realistic simulations and for collecting AR and VR assets. Neural fields have shown their effectiveness in learn…