no code implementations • 22 Mar 2024 • Jiayi Liu, Manolis Savva, Ali Mahdavi-Amiri
3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics.
no code implementations • 7 Mar 2024 • Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
Consequently, we can generate counting data for any type of object and count them in an unsupervised manner.
no code implementations • 5 Feb 2024 • Maham Tanveer, Yizhi Wang, Ruiqi Wang, Nanxuan Zhao, Ali Mahdavi-Amiri, Hao Zhang
We present AnaMoDiff, a novel diffusion-based method for 2D motion analogies that is applied to raw, unannotated videos of articulated characters.
no code implementations • 15 Dec 2023 • Jiayi Liu, Hou In Ivan Tam, Ali Mahdavi-Amiri, Manolis Savva
We address the challenge of generating 3D articulated objects in a controllable fashion.
no code implementations • 3 Dec 2023 • Yizhi Wang, Wallace Lira, Wenqi Wang, Ali Mahdavi-Amiri, Hao Zhang
Our key observation is that object slicing is more advantageous than altering views to reveal occluded structures.
no code implementations • 28 Nov 2023 • Mehdi Safaee, Aryan Mikaeili, Or Patashnik, Daniel Cohen-Or, Ali Mahdavi-Amiri
This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern.
1 code implementation • 2 Oct 2023 • Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh
To address this, we use latent diffusion models to create two types of synthetic data: one by removing pedestrians from real images, which generates ranked image pairs with a weak but reliable object quantity signal, and the other by generating synthetic images with a predetermined number of objects, offering a strong but noisy counting signal.
no code implementations • 1 Sep 2023 • Saeid Asgari Taghanaki, Aliasghar Khani, Amir Khasahmadi, Aditya Sanghi, Karl D. D. Willis, Ali Mahdavi-Amiri
These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier.
1 code implementation • ICCV 2023 • Jiayi Liu, Ali Mahdavi-Amiri, Manolis Savva
Our approach improves reconstruction relative to state-of-the-art baselines with a Chamfer-L1 distance reduction of 3. 94 (45. 2%) for objects and 26. 79 (84. 5%) for parts, and achieves 5% error rate for motion estimation across 10 object categories.
no code implementations • 29 May 2023 • Dingdong Yang, Yizhi Wang, Ali Mahdavi-Amiri, Hao Zhang
Our key codes and feature grids are jointly trained continuously with well-defined gradient flows, leading to high usage rates of the feature grids and improved generative modeling compared to discrete Vector Quantization (VQ).
no code implementations • ICCV 2023 • Aryan Mikaeili, Or Perel, Mehdi Safaee, Daniel Cohen-Or, Ali Mahdavi-Amiri
To ensure the generated output adheres to the provided sketches, we propose novel loss functions to generate the desired edits while preserving the density and radiance of the base instance.
1 code implementation • ICCV 2023 • Maham Tanveer, Yizhi Wang, Ali Mahdavi-Amiri, Hao Zhang
We introduce a novel method to automatically generate an artistic typography by stylizing one or more letter fonts to visually convey the semantics of an input word, while ensuring that the output remains readable.
1 code implementation • 20 Feb 2023 • Roy Hachnochi, Mingrui Zhao, Nadav Orzech, Rinon Gal, Ali Mahdavi-Amiri, Daniel Cohen-Or, Amit Haim Bermano
Diffusion models have enabled high-quality, conditional image editing capabilities.
1 code implementation • 30 Sep 2022 • Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani, Ali Gholami, Linh Tran, Ali Mahdavi-Amiri, Ghassan Hamarneh
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features.
1 code implementation • 3 Aug 2022 • Navjot Kaur, Cheng-Chun Lee, Ali Mostafavi, Ali Mahdavi-Amiri
In this work, a novel transformer-based network is proposed for assessing building damage.
1 code implementation • 13 Dec 2021 • Hang Zhou, Rui Ma, Ling-Xiao Zhang, Lin Gao, Ali Mahdavi-Amiri, Hao Zhang
Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch.
no code implementations • CVPR 2022 • Qimin Chen, Johannes Merz, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains.
no code implementations • 30 Jun 2021 • Himanshu Arora, Saurabh Mishra, Shichong Peng, Ke Li, Ali Mahdavi-Amiri
Shape completion is the problem of completing partial input shapes such as partial scans.
no code implementations • CVPR 2022 • Fenggen Yu, Zhiqin Chen, Manyi Li, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
no code implementations • 9 Apr 2021 • Jiongchao Jin, Arezou Fatemi, Wallace Lira, Fenggen Yu, Biao Leng, Rui Ma, Ali Mahdavi-Amiri, Hao Zhang
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research.
no code implementations • 22 Feb 2021 • Hessam Djavaherpour, Faramarz Samavati, Ali Mahdavi-Amiri, Fatemeh Yazdanbakhsh, Samuel Huron, Richard Levy, Yvonne Jansen, Lora Oehlberg
Physical representations of data offer physical and spatial ways of looking at, navigating, and interacting with data.
Graphics I.3.5; I.3.8
no code implementations • NeurIPS 2020 • Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.
no code implementations • 18 Apr 2018 • Fenggen Yu, Yan Zhang, Kai Xu, Ali Mahdavi-Amiri, Hao Zhang
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision.