Zobrazeno 1 - 10
of 5 948
pro vyhledávání: '"Niessner A"'
Autor:
Roessle, Barbara, Müller, Norman, Porzi, Lorenzo, Bulò, Samuel Rota, Kontschieder, Peter, Dai, Angela, Nießner, Matthias
We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. This enables effective generative 3D modeling, scaling to generation of entire room-scale scenes which can be very effi
Externí odkaz:
http://arxiv.org/abs/2410.13530
Detecting AI-generated images has become an extraordinarily difficult challenge as new generative architectures emerge on a daily basis with more and more capabilities and unprecedented realism. New versions of many commercial tools, such as DALLE, M
Externí odkaz:
http://arxiv.org/abs/2409.15875
We present 3DGS-LM, a new method that accelerates the reconstruction of 3D Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored Levenberg-Marquardt (LM). Existing methods reduce the optimization time by decreasing the number of G
Externí odkaz:
http://arxiv.org/abs/2409.12892
We present LT3SD, a novel latent diffusion model for large-scale 3D scene generation. Recent advances in diffusion models have shown impressive results in 3D object generation, but are limited in spatial extent and quality when extended to 3D scenes.
Externí odkaz:
http://arxiv.org/abs/2409.08215
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 extensive per-scene optimization for single or multiple styles, limiting the
Externí odkaz:
http://arxiv.org/abs/2408.13508
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 called distractors. Distractors have severe impact on the rendering qu
Externí odkaz:
http://arxiv.org/abs/2408.11697
Autor:
Müller, Norman, Schwarz, Katja, Roessle, Barbara, Porzi, Lorenzo, Bulò, Samuel Rota, Nießner, Matthias, Kontschieder, Peter
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 is highly ill-posed by nature, as there exist multiple, plausible expla
Externí odkaz:
http://arxiv.org/abs/2406.18524
Autor:
Kirschstein, Tobias, Giebenhain, Simon, Tang, Jiapeng, Georgopoulos, Markos, Nießner, Matthias
Learning 3D head priors from large 2D image collections is an important step towards high-quality 3D-aware human modeling. A core requirement is an efficient architecture that scales well to large-scale datasets and large image resolutions. Unfortuna
Externí odkaz:
http://arxiv.org/abs/2406.09377
Publikováno v:
SIGGRAPH Asia 2024 Conference Papers (SA Conference Papers '24), December 3-6, 2024, Tokyo, Japan
The creation of high-fidelity, digital versions of human heads is an important stepping stone in the process of further integrating virtual components into our everyday lives. Constructing such avatars is a challenging research problem, due to a high
Externí odkaz:
http://arxiv.org/abs/2405.19331
Autor:
Ma, Xianzheng, Bhalgat, Yash, Smart, Brandon, Chen, Shuai, Li, Xinghui, Ding, Jian, Gu, Jindong, Chen, Dave Zhenyu, Peng, Songyou, Bian, Jia-Wang, Torr, Philip H, Pollefeys, Marc, Nießner, Matthias, Reid, Ian D, Chang, Angel X., Laina, Iro, Prisacariu, Victor Adrian
As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overvie
Externí odkaz:
http://arxiv.org/abs/2405.10255