527 citations · 565 across the 13 of their papers we have counts for
16 papers
Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods
Panos Achlioptas, Alexandros Benetatos, Iordanis Fostiropoulos +1
In this work, we systematically study the problem of personalized text-to-image generation, where the output image is expected to portray information about specific human subjects.…
Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Models
Willi Menapace, Aliaksandr Siarohin, Stéphane Lathuilière +4
Neural video game simulators emerged as powerful tools to generate and edit videos. Their idea is to represent games as the evolution of an environment's state driven by the action…
LADIS: Language Disentanglement for 3D Shape Editing
Ian Huang, Panos Achlioptas, Tianyi Zhang +3
Natural language interaction is a promising direction for democratizing 3D shape design. However, existing methods for text-driven 3D shape editing face challenges in producing dec…
ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes
Ahmed Abdelreheem, Kyle Olszewski, Hsin-Ying Lee +2
The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data. In this paper, we curate a large-scale and complementary dataset extending…
Affection: Learning Affective Explanations for Real-World Visual Data
Panos Achlioptas, Maks Ovsjanikov, Leonidas Guibas +1
In this work, we explore the emotional reactions that real-world images tend to induce by using natural language as the medium to express the rationale behind an affective response…
Quantized GAN for Complex Music Generation from Dance Videos
Ye Zhu, Kyle Olszewski, Yu Wu +4
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos. Our proposed framework takes dan…
NeROIC: Neural Rendering of Objects from Online Image Collections
Zhengfei Kuang, Kyle Olszewski, Menglei Chai +3
We present a novel method to acquire object representations from online image collections, capturing high-quality geometry and material properties of arbitrary objects from photogr…
PartGlot: Learning Shape Part Segmentation from Language Reference Games
Juil Koo, Ian Huang, Panos Achlioptas +2
We introduce PartGlot, a neural framework and associated architectures for learning semantic part segmentation of 3D shape geometry, based solely on part referential language. We e…
ArtEmis: Affective Language for Visual Art
Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov +2
We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional e…
DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces
Minhyuk Sung, Zhenyu Jiang, Panos Achlioptas +2
Shape deformation is an important component in any geometry processing toolbox. The goal is to enable intuitive deformations of single or multiple shapes or to transfer example def…
Exploring Long Tail Visual Relationship Recognition with Large Vocabulary
Sherif Abdelkarim, Aniket Agarwal, Panos Achlioptas +5
Several approaches have been proposed in recent literature to alleviate the long-tail problem, mainly in object classification tasks. In this paper, we make the first large-scale s…
ShapeGlot: Learning Language for Shape Differentiation
Panos Achlioptas, Judy Fan, Robert X. D. Hawkins +2
In this work we explore how fine-grained differences between the shapes of common objects are expressed in language, grounded on images and 3D models of the objects. We first build…
OperatorNet: Recovering 3D Shapes From Difference Operators
Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas +2
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices. To this end we introduce a novel n…
Composite Shape Modeling via Latent Space Factorization
Anastasia Dubrovina, Fei Xia, Panos Achlioptas +3
We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling. Our method utilizes an auto-encoder-based pipeline, and…
Latent Space Representation for Shape Analysis and Learning
Ruqi Huang, Panos Achlioptas, Leonidas Guibas +1
We propose a novel shape representation useful for analyzing and processing shape collections, as well for a variety of learning and inference tasks. Unlike most approaches that ca…
Learning Representations and Generative Models for 3D Point Clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas +1
Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as poi…