Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Deng, Kangle"'
Autor:
Litman, Yehonathan, Patashnik, Or, Deng, Kangle, Agrawal, Aviral, Zawar, Rushikesh, De la Torre, Fernando, Tulsiani, Shubham
Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail to render accurately under new lighting conditions due to the intrinsic
Externí odkaz:
http://arxiv.org/abs/2409.15273
Autor:
Deng, Kangle, Omernick, Timothy, Weiss, Alexander, Ramanan, Deva, Zhu, Jun-Yan, Zhou, Tinghui, Agrawala, Maneesh
Manually creating textures for 3D meshes is time-consuming, even for expert visual content creators. We propose a fast approach for automatically texturing an input 3D mesh based on a user-provided text prompt. Importantly, our approach disentangles
Externí odkaz:
http://arxiv.org/abs/2402.13251
Recent advancements in robotics enable robots to accomplish complex assembly tasks. However, designing an assembly requires a non-trivial effort since a slight variation in the design could significantly affect the task feasibility. It is critical to
Externí odkaz:
http://arxiv.org/abs/2402.10711
We explore the task of embodied view synthesis from monocular videos of deformable scenes. Given a minute-long RGBD video of people interacting with their pets, we render the scene from novel camera trajectories derived from the in-scene motion of ac
Externí odkaz:
http://arxiv.org/abs/2304.12317
We propose pix2pix3D, a 3D-aware conditional generative model for controllable photorealistic image synthesis. Given a 2D label map, such as a segmentation or edge map, our model learns to synthesize a corresponding image from different viewpoints. T
Externí odkaz:
http://arxiv.org/abs/2302.08509
A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of
Externí odkaz:
http://arxiv.org/abs/2107.02791
We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers. Our approach builds on simple autoencoders that project out-of-sample data onto the distribu
Externí odkaz:
http://arxiv.org/abs/2001.04463
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dc4cfd9f85cec75ac1bd3d29743336c
https://doi.org/10.1109/cvpr52688.2022.01254
https://doi.org/10.1109/cvpr52688.2022.01254
