4 citations · 12 across the 7 of their papers we have counts for
7 papers
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Francis Engelmann, Fabian Manhardt, Michael Niemeyer +3
Large visual-language models (VLMs), like CLIP, enable open-set image segmentation to segment arbitrary concepts from an image in a zero-shot manner. This goes beyond the tradition…
Recent Trends in 3D Reconstruction of General Non-Rigid Scenes
Raza Yunus, Jan Eric Lenssen, Michael Niemeyer +7
Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthe…
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
Mohamad Shahbazi, Liesbeth Claessens, Michael Niemeyer +4
We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes. Based on a user-provided textual description and a 2D bounding box in…
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…