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Evangelos Kalogerakis

21 accepted papers

2026

DAGE: Dual-Stream Architecture for Efficient and Fine-Grained Geometry Estimation

Tuan Duc Ngo, Jiahui Huang, Seoung Wug Oh, Kevin Blackburn-Matzen, Evangelos Kalogerakis, Chuang Gan +1
CVPR 2026

Estimating accurate, view-consistent geometry and camera poses from uncalibrated multi-view/video inputs remains challenging--especially at high spatial resolutions and over long sequences. We present DAGE, a dual-stream transformer whose main novelty is to disentangle global coherence from fine det

Cited by 0Source ↗code ↗Scholar ↗
2025

DELTA: DENSE EFFICIENT LONG-RANGE 3D TRACKING FOR ANY VIDEO

Tuan Duc Ngo, Peiye Zhuang, Evangelos Kalogerakis, Chuang Gan, Sergey Tulyakov, Hsin-Ying Lee +1
ICLR 2025poster

Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. We introduce DELTA, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach lever…

Cited by 4Source ↗PDF ↗Scholar ↗
2025

GEOPARD: Geometric Pretraining for Articulation Prediction in 3D Shapes

Pradyumn Goyal, Dmitry Petrov, Sheldon Andrews, Yizhak Ben-Shabat, Hsueh-Ti Derek Liu, Evangelos Kalogerakis
ICCV 2025poster

We present GEOPARD, a transformer-based architecture for predicting articulation from a single static snapshot of a 3D shape. The key idea of our method is a pretraining strategy that allows our transformer to learn plausible candidate articulations for 3D shapes based on a geometric-driven search w…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts

Dmitry Petrov, Pradyumn Goyal, Divyansh Shivashok, Yuanming Tao, Melinos Averkiou, Evangelos Kalogerakis
CVPR 2025poster

We introduce ShapeWords, an approach for synthesizing images based on 3D shape guidance and text prompts.ShapeWords incorporates target 3D shape information within specialized tokens embedded together with the input text, effectively blending 3D shape awareness with textual context to guide the imag…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation

Vikas Thamizharasan, Difan Liu, Matthew Fisher, Nanxuan Zhao, Evangelos Kalogerakis, Michal Lukac
CVPR 2024poster

The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations such as vector graphics. Unfortunately due to their variable structure and scarcity of vector training data directly applying…

Cited by 6Source ↗PDF ↗Scholar ↗
2024

Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance

Phuc Nguyen, Tuan Duc Ngo, Evangelos Kalogerakis, Chuang Gan, Anh Tran, Cuong Pham +1
CVPR 2024poster

We introduce Open3DIS a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments exhibit diverse shapes scales and colors making precise instance-level identification a challenging task. Recent advancements in Open-Vocabu…

Cited by 50Source ↗PDF ↗code ↗Scholar ↗
2024

VecFusion: Vector Font Generation with Diffusion

Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang +2
CVPR 2024highlight

We present VecFusion a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generate…

Cited by 9Source ↗PDF ↗Scholar ↗
2022

APES: Articulated Part Extraction From Sprite Sheets

Zhan Xu, Matthew Fisher, Yang Zhou, Deepali Aneja, Rushikesh Dudhat, Li Yi +1
CVPR 2022poster

Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character po…

Cited by 5PDF ↗code ↗Scholar ↗
2022

Audio-Driven Neural Gesture Reenactment With Video Motion Graphs

Yang Zhou, Jimei Yang, Dingzeyu Li, Jun Saito, Deepali Aneja, Evangelos Kalogerakis
CVPR 2022poster

Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from a reference video through a novel video motion gr…

Cited by 19PDF ↗code ↗Scholar ↗
2022

MvDeCor: Multi-View Dense Correspondence Learning for Fine-Grained 3D Segmentation

Gopal Sharma, Kangxue Yin, Subhransu Maji, Evangelos Kalogerakis, Or Litany, Sanja Fidler
ECCV 2022poster

"We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. This is inspired by the observation that view-based surface representations are more effective at modeling high-resolution surface details and texture than their 3D counterparts based on…

Cited by 13Source ↗PDF ↗Scholar ↗
2021

BuildingNet: Learning To Label 3D Buildings

Pratheba Selvaraju, Mohamed Nabail, Marios Loizou, Maria Maslioukova, Melinos Averkiou, Andreas Andreou +2
ICCV 2021poster

We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, and (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives. To create our dataset, we used crowdsourcing com…

Cited by 49PDF ↗code ↗Scholar ↗
2021

Neural Strokes: Stylized Line Drawing of 3D Shapes

Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis
ICCV 2021poster

This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes, with variations in stroke thickness, deformation, and color learned from an artist's style. The model is fully differen…

Cited by 24PDF ↗code ↗Scholar ↗
2020

Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions

Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik Learned-Miller +2
ECCV 2020poster

The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer from relatively small training sets, creating the need for statistically efficient methods to learn 3D shape representatio…

Cited by 57Source ↗PDF ↗code ↗Scholar ↗
2020

Neural Contours: Learning to Draw Lines From 3D Shapes

Difan Liu, Mohamed Nabail, Aaron Hertzmann, Evangelos Kalogerakis
CVPR 2020poster

This paper introduces a method for learning to generate line drawings from 3D models. Our architecture incorporates a differentiable module operating on geometric features of the 3D model, and an image-based module operating on view-based shape representations. At test time, geometric and view-based…

Cited by 44PDF ↗code ↗Scholar ↗
2020

ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, Radomír Měch
ECCV 2020poster

We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives. ParSeNet is trained on a large-scale dataset of man-made 3D shapes and captures high-level semant…

Cited by 172Source ↗PDF ↗code ↗Scholar ↗
2019

SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation

Yang Zhou, Zachary While, Evangelos Kalogerakis
ICCV 2019poster

In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings. Given an input, potentially incomplete, 3D scene and a query location, our method predicts a probability distribution over object types that fit well in that l…

Cited by 106PDF ↗code ↗Scholar ↗
2018

CSGNet: Neural Shape Parser for Constructive Solid Geometry

Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji
CVPR 2018poster

We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques f…

Cited by 0Source ↗PDF ↗Scholar ↗
2018

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang +1
CVPR 2018poster

We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice. Naively applying convolutions on this lattice scales poorly, both in terms of memory and computational cost, as the siz…

Cited by 914Source ↗PDF ↗code ↗Scholar ↗
2017

3D Shape Segmentation With Projective Convolutional Networks

Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, Siddhartha Chaudhuri
CVPR 2017oral

This paper introduces a deep architecture for segmenting 3D objects into their labeled semantic parts. Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are…

Cited by 469PDF ↗Scholar ↗
2017

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, Yizhou Yu
ICCV 2017spotlight

We propose a data-driven method for recovering missing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long…

Cited by 367PDF ↗Scholar ↗
2015

Multi-View Convolutional Neural Networks for 3D Shape Recognition

Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller
ICCV 2015poster

A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We addre…

Cited by 4470PDF ↗Scholar ↗