Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Kleiman, Yanir"'
Autor:
Bensadoun, Raphael, Monnier, Tom, Kleiman, Yanir, Kokkinos, Filippos, Siddiqui, Yawar, Kariya, Mahendra, Harosh, Omri, Shapovalov, Roman, Graham, Benjamin, Garreau, Emilien, Karnewar, Animesh, Cao, Ang, Azuri, Idan, Makarov, Iurii, Le, Eric-Tuan, Toisoul, Antoine, Novotny, David, Gafni, Oran, Neverova, Natalia, Vedaldi, Andrea
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports physically-based
Externí odkaz:
http://arxiv.org/abs/2407.02599
Autor:
Siddiqui, Yawar, Monnier, Tom, Kokkinos, Filippos, Kariya, Mahendra, Kleiman, Yanir, Garreau, Emilien, Gafni, Oran, Neverova, Natalia, Vedaldi, Andrea, Shapovalov, Roman, Novotny, David
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared to works that bake shading in the 3D object's appearance, AssetGen out
Externí odkaz:
http://arxiv.org/abs/2407.02445
Autor:
Bensadoun, Raphael, Kleiman, Yanir, Azuri, Idan, Harosh, Omri, Vedaldi, Andrea, Neverova, Natalia, Gafni, Oran
The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for
Externí odkaz:
http://arxiv.org/abs/2407.02430
Autor:
Shapovalov, Roman, Kleiman, Yanir, Rocco, Ignacio, Novotny, David, Vedaldi, Andrea, Chen, Changan, Kokkinos, Filippos, Graham, Ben, Neverova, Natalia
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially. Each scene is filmed in high production quality, from different viewpoints with several static cameras, as well as wearable action cameras, and record
Externí odkaz:
http://arxiv.org/abs/2307.12067
Autor:
Kleiman, Yanir, Ovsjanikov, Maks
We present a robust method to find region-level correspondences between shapes, which are invariant to changes in geometry and applicable across multiple shape representations. We generate simplified shape graphs by jointly decomposing the shapes, an
Externí odkaz:
http://arxiv.org/abs/1710.05592
In this paper, we propose PCPNet, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, e.g., for shape classificat
Externí odkaz:
http://arxiv.org/abs/1710.04954
Understanding semantic similarity among images is the core of a wide range of computer vision applications. An important step towards this goal is to collect and learn human perceptions. Interestingly, the semantic context of images is often ambiguou
Externí odkaz:
http://arxiv.org/abs/1703.09928
Autor:
Zeng, Qiong, Chen, Wenzheng, Han, Zhuo, Shi, Mingyi, Kleiman, Yanir, Cohen-Or, Daniel, Chen, Baoquan, Li, Yangyan
Publikováno v:
In Visual Informatics September 2018 2(3):181-189
Autor:
Kleiman, Yanir1, Ovsjanikov, Maks1
Publikováno v:
Computer Graphics Forum. Feb2019, Vol. 38 Issue 1, p7-20. 14p. 11 Diagrams, 3 Charts, 2 Graphs.
Publikováno v:
Computer Graphics Forum. May2018, Vol. 37 Issue 2, p75-85. 11p. 3 Color Photographs, 1 Diagram, 6 Graphs.