Zobrazeno 1 - 10
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pro vyhledávání: '"Vedaldi, A"'
Autor:
Held, Jan, Vandeghen, Renaud, Hamdi, Abdullah, Deliege, Adrien, Cioppa, Anthony, Giancola, Silvio, Vedaldi, Andrea, Ghanem, Bernard, Van Droogenbroeck, Marc
Recent advances in radiance field reconstruction, such as 3D Gaussian Splatting (3DGS), have achieved high-quality novel view synthesis and fast rendering by representing scenes with compositions of Gaussian primitives. However, 3D Gaussians present
Externí odkaz:
http://arxiv.org/abs/2411.14974
Autor:
Chen, Yuedong, Zheng, Chuanxia, Xu, Haofei, Zhuang, Bohan, Vedaldi, Andrea, Cham, Tat-Jen, Cai, Jianfei
We introduce MVSplat360, a feed-forward approach for 360{\deg} novel view synthesis (NVS) of diverse real-world scenes, using only sparse observations. This setting is inherently ill-posed due to minimal overlap among input views and insufficient vis
Externí odkaz:
http://arxiv.org/abs/2411.04924
Autor:
Karaev, Nikita, Makarov, Iurii, Wang, Jianyuan, Neverova, Natalia, Vedaldi, Andrea, Rupprecht, Christian
Most state-of-the-art point trackers are trained on synthetic data due to the difficulty of annotating real videos for this task. However, this can result in suboptimal performance due to the statistical gap between synthetic and real videos. In orde
Externí odkaz:
http://arxiv.org/abs/2410.11831
Generating high-quality 3D content from text, single images, or sparse view images remains a challenging task with broad applications. Existing methods typically employ multi-view diffusion models to synthesize multi-view images, followed by a feed-f
Externí odkaz:
http://arxiv.org/abs/2410.00890
Image-based 3D object detection is widely employed in applications such as autonomous vehicles and robotics, yet current systems struggle with generalisation due to complex problem setup and limited training data. We introduce a novel pipeline that d
Externí odkaz:
http://arxiv.org/abs/2408.12747
Autor:
Bhalgat, Yash, Tschernezki, Vadim, Laina, Iro, Henriques, João F., Vedaldi, Andrea, Zisserman, Andrew
Egocentric videos present unique challenges for 3D scene understanding due to rapid camera motion, frequent object occlusions, and limited object visibility. This paper introduces a novel approach to instance segmentation and tracking in first-person
Externí odkaz:
http://arxiv.org/abs/2408.09860
We present Puppet-Master, an interactive video generative model that can serve as a motion prior for part-level dynamics. At test time, given a single image and a sparse set of motion trajectories (i.e., drags), Puppet-Master can synthesize a video d
Externí odkaz:
http://arxiv.org/abs/2408.04631
Canonical surface mapping generalizes keypoint detection by assigning each pixel of an object to a corresponding point in a 3D template. Popularised by DensePose for the analysis of humans, authors have since attempted to apply the concept to more ca
Externí odkaz:
http://arxiv.org/abs/2407.18907
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