Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Xu Zexiang"'
Autor:
Hu, Hanzhe, Yin, Tianwei, Luan, Fujun, Hu, Yiwei, Tan, Hao, Xu, Zexiang, Bi, Sai, Tulsiani, Shubham, Zhang, Kai
We present Turbo3D, an ultra-fast text-to-3D system capable of generating high-quality Gaussian splatting assets in under one second. Turbo3D employs a rapid 4-step, 4-view diffusion generator and an efficient feed-forward Gaussian reconstructor, bot
Externí odkaz:
http://arxiv.org/abs/2412.04470
Autor:
Kuang, Zhengfei, Zhang, Tianyuan, Zhang, Kai, Tan, Hao, Bi, Sai, Hu, Yiwei, Xu, Zexiang, Hasan, Milos, Wetzstein, Gordon, Luan, Fujun
We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training data. Instead of relying on large-scale annotated
Externí odkaz:
http://arxiv.org/abs/2411.17249
Autor:
Chou, Gene, Zhang, Kai, Bi, Sai, Tan, Hao, Xu, Zexiang, Luan, Fujun, Hariharan, Bharath, Snavely, Noah
We address the problem of generating videos from unposed internet photos. A handful of input images serve as keyframes, and our model interpolates between them to simulate a path moving between the cameras. Given random images, a model's ability to c
Externí odkaz:
http://arxiv.org/abs/2411.13549
We propose a novel online, point-based 3D reconstruction method from posed monocular RGB videos. Our model maintains a global point cloud representation of the scene, continuously updating the features and 3D locations of points as new images are obs
Externí odkaz:
http://arxiv.org/abs/2410.23245
Autor:
Jin, Haian, Jiang, Hanwen, Tan, Hao, Zhang, Kai, Bi, Sai, Zhang, Tianyuan, Luan, Fujun, Snavely, Noah, Xu, Zexiang
We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which encodes input image t
Externí odkaz:
http://arxiv.org/abs/2410.17242
Autor:
Ziwen, Chen, Tan, Hao, Zhang, Kai, Bi, Sai, Luan, Fujun, Hong, Yicong, Fuxin, Li, Xu, Zexiang
We propose Long-LRM, a generalizable 3D Gaussian reconstruction model that is capable of reconstructing a large scene from a long sequence of input images. Specifically, our model can process 32 source images at 960x540 resolution within only 1.3 sec
Externí odkaz:
http://arxiv.org/abs/2410.12781
Autor:
Zhang, Tianyuan, Kuang, Zhengfei, Jin, Haian, Xu, Zexiang, Bi, Sai, Tan, Hao, Zhang, He, Hu, Yiwei, Hasan, Milos, Freeman, William T., Zhang, Kai, Luan, Fujun
We propose RelitLRM, a Large Reconstruction Model (LRM) for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations from sparse (4-8) posed images captured under unknown static lighting. Unlike prior inverse
Externí odkaz:
http://arxiv.org/abs/2410.06231
Autor:
Cai, Guangyan, Luan, Fujun, Hašan, Miloš, Zhang, Kai, Bi, Sai, Xu, Zexiang, Georgiev, Iliyan, Zhao, Shuang
Glossy objects present a significant challenge for 3D reconstruction from multi-view input images under natural lighting. In this paper, we introduce PBIR-NIE, an inverse rendering framework designed to holistically capture the geometry, material att
Externí odkaz:
http://arxiv.org/abs/2408.06878
Autor:
Xie, Desai, Bi, Sai, Shu, Zhixin, Zhang, Kai, Xu, Zexiang, Zhou, Yi, Pirk, Sören, Kaufman, Arie, Sun, Xin, Tan, Hao
We present LRM-Zero, a Large Reconstruction Model (LRM) trained entirely on synthesized 3D data, achieving high-quality sparse-view 3D reconstruction. The core of LRM-Zero is our procedural 3D dataset, Zeroverse, which is automatically synthesized fr
Externí odkaz:
http://arxiv.org/abs/2406.09371
Autor:
Jin, Haian, Li, Yuan, Luan, Fujun, Xiangli, Yuanbo, Bi, Sai, Zhang, Kai, Xu, Zexiang, Sun, Jin, Snavely, Noah
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting. Many prior methods either support only specific categories of images, such as portraits, or require special c
Externí odkaz:
http://arxiv.org/abs/2406.07520