Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Wu, Rundi"'
Autor:
Van Hoorick, Basile, Wu, Rundi, Ozguroglu, Ege, Sargent, Kyle, Liu, Ruoshi, Tokmakov, Pavel, Dave, Achal, Zheng, Changxi, Vondrick, Carl
Accurate reconstruction of complex dynamic scenes from just a single viewpoint continues to be a challenging task in computer vision. Current dynamic novel view synthesis methods typically require videos from many different camera viewpoints, necessi
Externí odkaz:
http://arxiv.org/abs/2405.14868
Autor:
Zhang, Tianyuan, Yu, Hong-Xing, Wu, Rundi, Feng, Brandon Y., Zheng, Changxi, Snavely, Noah, Wu, Jiajun, Freeman, William T.
Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a significant challenge. Unlike unconditional or text-conditioned dynamics g
Externí odkaz:
http://arxiv.org/abs/2404.13026
Autor:
Wu, Rundi, Mildenhall, Ben, Henzler, Philipp, Park, Keunhong, Gao, Ruiqi, Watson, Daniel, Srinivasan, Pratul P., Verbin, Dor, Barron, Jonathan T., Poole, Ben, Holynski, Aleksander
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However, recovering a high-quality NeRF typically requires tens to hundreds of input images, resulting in a time-consumi
Externí odkaz:
http://arxiv.org/abs/2312.02981
Synthesizing novel 3D models that resemble the input example has long been pursued by graphics artists and machine learning researchers. In this paper, we present Sin3DM, a diffusion model that learns the internal patch distribution from a single 3D
Externí odkaz:
http://arxiv.org/abs/2305.15399
Autor:
Liu, Ruoshi, Wu, Rundi, Van Hoorick, Basile, Tokmakov, Pavel, Zakharov, Sergey, Vondrick, Carl
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To perform novel view synthesis in this under-constrained setting, we capitalize on the geometric priors that large-scale diffusion mo
Externí odkaz:
http://arxiv.org/abs/2303.11328
Implicit Neural Spatial Representation (INSR) has emerged as an effective representation of spatially-dependent vector fields. This work explores solving time-dependent PDEs with INSR. Classical PDE solvers introduce both temporal and spatial discret
Externí odkaz:
http://arxiv.org/abs/2210.00124
Autor:
Wu, Rundi, Zheng, Changxi
Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that learns from only a single reference 3D shape. Specifically, we pre
Externí odkaz:
http://arxiv.org/abs/2208.02946
Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a dra
Externí odkaz:
http://arxiv.org/abs/2105.09492
We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications. Our approach is based on a key observation about human speech: there is often a short pause between each sentence o
Externí odkaz:
http://arxiv.org/abs/2010.12013
Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. These methods, however, only complete the partial shape with a single output, ignori
Externí odkaz:
http://arxiv.org/abs/2003.07717