321 citations · 565 across the 12 of their papers we have counts for
16 papers
TextMesh: Generation of Realistic 3D Meshes From Text Prompts
Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni +2
The ability to generate highly realistic 2D images from mere text prompts has recently made huge progress in terms of speed and quality, thanks to the advent of image diffusion mod…
NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM
Hidenobu Matsuki, Keisuke Tateno, Michael Niemeyer +1
Neural field-based 3D representations have recently been adopted in many areas including SLAM systems. Current neural SLAM or online mapping systems lead to impressive results in t…
DreamBooth3D: Subject-Driven Text-to-3D Generation
Amit Raj, Srinivas Kaza, Ben Poole +9
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject. Our approach combines recent advances in…
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Marie-Julie Rakotosaona, Fabian Manhardt, Diego Martin Arroyo +3
With the introduction of Neural Radiance Fields (NeRFs), novel view synthesis has recently made a big leap forward. At the core, NeRF proposes that each 3D point can emit radiance,…
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Katja Schwarz, Axel Sauer, Michael Niemeyer +2
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields. While demonstrating impressive results, querying an MLP for every samp…
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Zehao Yu, Songyou Peng, Michael Niemeyer +2
In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these ap…
Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers
Jan van den Brand, Yu Gao, Arun Jambulapati +4
We make several advances broadly related to the maintenance of electrical flows in weighted graphs undergoing dynamic resistance updates, including: 1. More efficient dynamic spect…
Shape As Points: A Differentiable Poisson Solver
Songyou Peng, Chiyu "Max" Jiang, Yiyi Liao +3
In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit…
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields
Michael Niemeyer, Andreas Geiger
Tremendous progress in deep generative models has led to photorealistic image synthesis. While achieving compelling results, most approaches operate in the two-dimensional image do…
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
Michael Niemeyer, Andreas Geiger
Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable.…
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer +1
While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Thus, the…
Learning Implicit Surface Light Fields
Michael Oechsle, Michael Niemeyer, Lars Mescheder +2
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to repres…
Convolutional Occupancy Networks
Songyou Peng, Michael Niemeyer, Lars Mescheder +2
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited t…
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle +1
Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently…
Texture Fields: Learning Texture Representations in Function Space
Michael Oechsle, Lars Mescheder, Michael Niemeyer +2
In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly r…
Occupancy Networks: Learning 3D Reconstruction in Function Space
Lars Mescheder, Michael Oechsle, Michael Niemeyer +2
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representat…