41 citations · 115 across the 15 of their papers we have counts for
17 papers
3D Space as a Scratchpad for Editable Text-to-Image Generation
Oindrila Saha, Vojtech Krs, Radomir Mech +3
Recent progress in large language models (LLMs) has shown that reasoning improves when intermediate thoughts are externalized into explicit workspaces, such as chain-of-thought tra…
SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation
Oindrila Saha, Vojtech Krs, Radomir Mech +3
We present SIGMA-GEN, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-GEN is the first to enable single-pass multi-subject identi…
Accidental Turntables: Learning 3D Pose by Watching Objects Turn
Zezhou Cheng, Matheus Gadelha, Subhransu Maji
We propose a technique for learning single-view 3D object pose estimation models by utilizing a new source of data -- in-the-wild videos where objects turn. Such videos are prevale…
Recovering Detail in 3D Shapes Using Disparity Maps
Marissa Ramirez de Chanlatte, Matheus Gadelha, Thibault Groueix +1
We present a fine-tuning method to improve the appearance of 3D geometries reconstructed from single images. We leverage advances in monocular depth estimation to obtain disparity…
PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos
Yiming Xie, Matheus Gadelha, Fengting Yang +2
We present PlanarRecon -- a novel framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video. Unlike previous works that detect planes i…
ANISE: Assembly-based Neural Implicit Surface rEconstruction
Dmitry Petrov, Matheus Gadelha, Radomir Mech +1
We present ANISE, a method that reconstructs a 3D~shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. The shape…
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
Gopal Sharma, Bidya Dash, Aruni RoyChowdhury +7
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks. PriFit combines geometric primitive fitting with point-based rep…
Deep Manifold Prior
Matheus Gadelha, Rui Wang, Subhransu Maji
We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent starting…
Learning Generative Models of Shape Handles
Matheus Gadelha, Giorgio Gori, Duygu Ceylan +5
We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, s…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
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 fr…
Inferring 3D Shapes from Image Collections using Adversarial Networks
Matheus Gadelha, Aartika Rai, Subhransu Maji +1
We investigate the problem of learning a probabilistic distribution over three-dimensional shapes given two-dimensional views of multiple objects taken from unknown viewpoints. Our…
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji +1
The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For "inference", gradi…
A Deeper Look at 3D Shape Classifiers
Jong-Chyi Su, Matheus Gadelha, Rui Wang +1
We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial tra…
Multiresolution Tree Networks for 3D Point Cloud Processing
Matheus Gadelha, Rui Wang, Subhransu Maji
We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-…
Shape Generation using Spatially Partitioned Point Clouds
Matheus Gadelha, Subhransu Maji, Rui Wang
We propose a method to generate 3D shapes using point clouds. Given a point-cloud representation of a 3D shape, our method builds a kd-tree to spatially partition the points. This…
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis +2
We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense…
3D Shape Induction from 2D Views of Multiple Objects
Matheus Gadelha, Subhransu Maji, Rui Wang
In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints.…