Search Results for author: Ashkan Mirzaei

Found 9 papers, 1 papers with code

3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes

no code implementations9 Jul 2024 Nicolas Moenne-Loccoz, Ashkan Mirzaei, Or Perel, Riccardo de Lutio, Janick Martinez Esturo, Gavriel State, Sanja Fidler, Nicholas Sharp, Zan Gojcic

The benefits of ray tracing are well-known in computer graphics: processing incoherent rays for secondary lighting effects such as shadows and reflections, rendering from highly-distorted cameras common in robotics, stochastically sampling rays, and more.

L4GM: Large 4D Gaussian Reconstruction Model

no code implementations14 Jun 2024 Jiawei Ren, Kevin Xie, Ashkan Mirzaei, Hanxue Liang, Xiaohui Zeng, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung Wook Kim, Huan Ling

We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second.

RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting

no code implementations16 Apr 2024 Ashkan Mirzaei, Riccardo de Lutio, Seung Wook Kim, David Acuna, Jonathan Kelly, Sanja Fidler, Igor Gilitschenski, Zan Gojcic

In this work, we propose an approach for 3D scene inpainting -- the task of coherently replacing parts of the reconstructed scene with desired content.

3D Inpainting Image Inpainting

Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations

no code implementations27 Oct 2023 Tristan Aumentado-Armstrong, Ashkan Mirzaei, Marcus A. Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G. Derpanis, Igor Gilitschenski

The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs, as the AE can correct certain visual artifacts, while rendering over three times faster.

Continual Learning Novel View Synthesis

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

no code implementations17 Aug 2023 Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G. Derpanis, Igor Gilitschenski

A field is trained on relevance maps of training views, denoted as the relevance field, defining the 3D region within which modifications should be made.

Denoising Image Generation

CAMM: Building Category-Agnostic and Animatable 3D Models from Monocular Videos

1 code implementation14 Apr 2023 Tianshu Kuai, Akash Karthikeyan, Yash Kant, Ashkan Mirzaei, Igor Gilitschenski

Animating an object in 3D often requires an articulated structure, e. g. a kinematic chain or skeleton of the manipulated object with proper skinning weights, to obtain smooth movements and surface deformations.

Object Surface Reconstruction

LaTeRF: Label and Text Driven Object Radiance Fields

no code implementations4 Jul 2022 Ashkan Mirzaei, Yash Kant, Jonathan Kelly, Igor Gilitschenski

In this paper we introduce LaTeRF, a method for extracting an object of interest from a scene given 2D images of the entire scene, known camera poses, a natural language description of the object, and a set of point-labels of object and non-object points in the input images.

Object

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