372 citations · 641 across the 71 of their papers we have counts for
10 papers · 1 filter
GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space
Souhaib Attaiki, Paul Guerrero, Duygu Ceylan +2
We train a feed-forward text-to-3D diffusion generator for human characters using only single-view 2D data for supervision. Existing 3D generative models cannot yet match the fidel…
ZeroKey: Point-Level Reasoning and Zero-Shot 3D Keypoint Detection from Large Language Models
Bingchen Gong, Diego Gomez, Abdullah Hamdi +4
We propose a novel zero-shot approach for keypoint detection on 3D shapes. Point-level reasoning on visual data is challenging as it requires precise localization capability, posin…
Deformation Recovery: Localized Learning for Detail-Preserving Deformations
Ramana Sundararaman, Nicolas Donati, Simone Melzi +2
We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that requ…
DeBaRA: Denoising-Based 3D Room Arrangement Generation
Léopold Maillard, Nicolas Sereyjol-Garros, Tom Durand +1
Generating realistic and diverse layouts of furnished indoor 3D scenes unlocks multiple interactive applications impacting a wide range of industries. The inherent complexity of ob…
Self-Supervised Dual Contouring
Ramana Sundararaman, Roman Klokov, Maks Ovsjanikov
Learning-based isosurface extraction methods have recently emerged as a robust and efficient alternative to axiomatic techniques. However, the vast majority of such approaches rely…
Memory-Scalable and Simplified Functional Map Learning
Robin Magnet, Maks Ovsjanikov
Deep functional maps have emerged in recent years as a prominent learning-based framework for non-rigid shape matching problems. While early methods in this domain only focused on…
PoNQ: a Neural QEM-based Mesh Representation
Nissim Maruani, Maks Ovsjanikov, Pierre Alliez +1
Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applicatio…
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
Souhaib Attaiki, Maks Ovsjanikov
We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data. SNK operate…
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning
Souhail Hadgi, Lei Li, Maks Ovsjanikov
Transfer learning has long been a key factor in the advancement of many fields including 2D image analysis. Unfortunately, its applicability in 3D data processing has been relative…
Smoothed Graph Contrastive Learning via Seamless Proximity Integration
Maysam Behmanesh, Maks Ovsjanikov
Graph contrastive learning (GCL) aligns node representations by classifying node pairs into positives and negatives using a selection process that typically relies on establishing…