298 citations · 881 across the 22 of their papers we have counts for
26 papers
Gen2Physics: Grounding Generated 3D Meshes in Physics via Multi-View Material Decomposition
Mauro Comi, Jordi Serrano Berbel, Kevis-Kokitsi Maninis +2
While state-of-the-art generative models produce high-fidelity 3D meshes, these outputs lack the physical properties required for interactive simulation, gaming, or robotics. We in…
TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment
Bingyi Cao, Koert Chen, Kevis-Kokitsi Maninis +16
Recent progress in vision-language pretraining has enabled significant improvements to many downstream computer vision applications, such as classification, retrieval, segmentation…
Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer
Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan +169
General-purpose robots need a deep understanding of the physical world, advanced reasoning, and general and dexterous control. This report introduces the latest generation of the G…
EgoCast: Forecasting Egocentric Human Pose in the Wild
Maria Escobar, Juanita Puentes, Cristhian Forigua +3
Accurately estimating and forecasting human body pose is important for enhancing the user's sense of immersion in Augmented Reality. Addressing this need, our paper introduces EgoC…
TIPS: Text-Image Pretraining with Spatial awareness
Kevis-Kokitsi Maninis, Kaifeng Chen, Soham Ghosh +11
While image-text representation learning has become very popular in recent years, existing models tend to lack spatial awareness and have limited direct applicability for dense und…
OmniNOCS: A unified NOCS dataset and model for 3D lifting of 2D objects
Akshay Krishnan, Abhijit Kundu, Kevis-Kokitsi Maninis +2
We propose OmniNOCS, a large-scale monocular dataset with 3D Normalized Object Coordinate Space (NOCS) maps, object masks, and 3D bounding box annotations for indoor and outdoor sc…
Probing the 3D Awareness of Visual Foundation Models
Mohamed El Banani, Amit Raj, Kevis-Kokitsi Maninis +7
Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their train…
EgoCOL: Egocentric Camera pose estimation for Open-world 3D object Localization @Ego4D challenge 2023
Cristhian Forigua, Maria Escobar, Jordi Pont-Tuset +2
We present EgoCOL, an egocentric camera pose estimation method for open-world 3D object localization. Our method leverages sparse camera pose reconstructions in a two-fold manner,…
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Varun Jampani, Kevis-Kokitsi Maninis, Andreas Engelhardt +13
Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on…
Estimating Generic 3D Room Structures from 2D Annotations
Denys Rozumnyi, Stefan Popov, Kevis-Kokitsi Maninis +2
Indoor rooms are among the most common use cases in 3D scene understanding. Current state-of-the-art methods for this task are driven by large annotated datasets. Room layouts are…
CAD-Estate: Large-scale CAD Model Annotation in RGB Videos
Kevis-Kokitsi Maninis, Stefan Popov, Matthias Nießner +1
We propose a method for annotating videos of complex multi-object scenes with a globally-consistent 3D representation of the objects. We annotate each object with a CAD model from…
RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers
Michał J. Tyszkiewicz, Kevis-Kokitsi Maninis, Stefan Popov +1
We propose a transformer-based neural network architecture for multi-object 3D reconstruction from RGB videos. It relies on two alternative ways to represent its knowledge: as a gl…
Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videos
Kevis-Kokitsi Maninis, Stefan Popov, Matthias Nießner +1
We address the task of aligning CAD models to a video sequence of a complex scene containing multiple objects. Our method can process arbitrary videos and fully automatically recov…
The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation
Sergi Caelles, Jordi Pont-Tuset, Federico Perazzi +3
We present the 2019 DAVIS Challenge on Video Object Segmentation, the third edition of the DAVIS Challenge series, a public competition designed for the task of Video Object Segmen…
Attentive Single-Tasking of Multiple Tasks
Kevis-Kokitsi Maninis, Ilija Radosavovic, Iasonas Kokkinos
In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to…
Iterative Deep Learning for Road Topology Extraction
Carles Ventura, Jordi Pont-Tuset, Sergi Caelles +2
This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the…
The 2018 DAVIS Challenge on Video Object Segmentation
Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis +4
We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 201…
Iterative Deep Learning for Network Topology Extraction
Carles Ventura, Jordi Pont-Tuset, Sergi Caelles +2
This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks. Building on top of a global model that performs a dense se…
Detection-aided liver lesion segmentation using deep learning
Miriam Bellver, Kevis-Kokitsi Maninis, Jordi Pont-Tuset +3
A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the…
Deep Extreme Cut: From Extreme Points to Object Segmentation
Kevis-Kokitsi Maninis, Sergi Caelles, Jordi Pont-Tuset +1
This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do s…
Video Object Segmentation Without Temporal Information
Kevis-Kokitsi Maninis, Sergi Caelles, Yuhua Chen +4
Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video fra…
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery
Thomas Probst, Kevis-Kokitsi Maninis, Ajad Chhatkuli +3
Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference.…
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez +1
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neur…
One-Shot Video Object Segmentation
Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset +3
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We pr…