8 citations · 19 across the 12 of their papers we have counts for
12 papers
G3DST: Generalizing 3D Style Transfer with Neural Radiance Fields across Scenes and Styles
Adil Meric, Umut Kocasari, Matthias Nießner +1
Neural Radiance Fields (NeRF) have emerged as a powerful tool for creating highly detailed and photorealistic scenes. Existing methods for NeRF-based 3D style transfer need extensi…
Robust 3D Gaussian Splatting for Novel View Synthesis in Presence of Distractors
Paul Ungermann, Armin Ettenhofer, Matthias Nießner +1
3D Gaussian Splatting has shown impressive novel view synthesis results; nonetheless, it is vulnerable to dynamic objects polluting the input data of an otherwise static scene, so…
MultiDiff: Consistent Novel View Synthesis from a Single Image
Norman Müller, Katja Schwarz, Barbara Roessle +4
We introduce MultiDiff, a novel approach for consistent novel view synthesis of scenes from a single RGB image. The task of synthesizing novel views from a single reference image i…
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models
Xianzheng Ma, Brandon Smart, Yash Bhalgat +14
As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interact…
LightIt: Illumination Modeling and Control for Diffusion Models
Peter Kocsis, Julien Philip, Kalyan Sunkavalli +2
We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspect…
Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation
Yujin Chen, Yinyu Nie, Benjamin Ummenhofer +4
We present Mesh2NeRF, an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. Many 3D generative approaches represent 3D scenes as radiance…
PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models
Antonio Alliegro, Yawar Siddiqui, Tatiana Tommasi +1
We introduce PolyDiff, the first diffusion-based approach capable of directly generating realistic and diverse 3D polygonal meshes. In contrast to methods that use alternate 3D sha…
Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation
Shentong Mo, Enze Xie, Yue Wu +3
Diffusion Transformers have recently shown remarkable effectiveness in generating high-quality 3D point clouds. However, training voxel-based diffusion models for high-resolution 3…
Raising the Bar of AI-generated Image Detection with CLIP
Davide Cozzolino, Giovanni Poggi, Riccardo Corvi +2
The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection str…
PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing
Natalia Soboleva, Olga Gorbunova, Maria Ivanova +4
Surface reconstruction with preservation of geometric features is a challenging computer vision task. Despite significant progress in implicit shape reconstruction, state-of-the-ar…
SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors
Dave Zhenyu Chen, Haoxuan Li, Hsin-Ying Lee +2
We propose SceneTex, a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous m…
Generating Context-Aware Natural Answers for Questions in 3D Scenes
Mohammed Munzer Dwedari, Matthias Niessner, Dave Zhenyu Chen
3D question answering is a young field in 3D vision-language that is yet to be explored. Previous methods are limited to a pre-defined answer space and cannot generate answers natu…