Zobrazeno 1 - 10
of 3 166
pro vyhledávání: '"P, Kasten"'
Generating 3D visual scenes is at the forefront of visual generative AI, but current 3D generation techniques struggle with generating scenes with multiple high-resolution objects. Here we introduce Lay-A-Scene, which solves the task of Open-set 3D O
Externí odkaz:
http://arxiv.org/abs/2406.00687
Multiview Structure from Motion is a fundamental and challenging computer vision problem. A recent deep-based approach was proposed utilizing matrix equivariant architectures for the simultaneous recovery of camera pose and 3D scene structure from la
Externí odkaz:
http://arxiv.org/abs/2404.14280
This paper addresses the long-standing challenge of reconstructing 3D structures from videos with dynamic content. Current approaches to this problem were not designed to operate on casual videos recorded by standard cameras or require a long optimiz
Externí odkaz:
http://arxiv.org/abs/2404.07097
Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portray the same subject across diverse prompts remains chall
Externí odkaz:
http://arxiv.org/abs/2402.03286
We present a new method for text-driven motion transfer - synthesizing a video that complies with an input text prompt describing the target objects and scene while maintaining an input video's motion and scene layout. Prior methods are confined to t
Externí odkaz:
http://arxiv.org/abs/2311.17009
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-12 (2024)
Abstract During continuous tasks, humans show spontaneous fluctuations in performance, putatively caused by varying attentional resources allocated to process external information. If neural resources are used to process other, presumably “internal
Externí odkaz:
https://doaj.org/article/3c9c7f9927e84f14af6738adbf1a5659
The majority of existing large 3D shape datasets contain meshes that lend themselves extremely well to visual applications such as rendering, yet tend to be topologically invalid (i.e, contain non-manifold edges and vertices, disconnected components,
Externí odkaz:
http://arxiv.org/abs/2307.00690
Point-cloud data collected in real-world applications are often incomplete. Data is typically missing due to objects being observed from partial viewpoints, which only capture a specific perspective or angle. Additionally, data can be incomplete due
Externí odkaz:
http://arxiv.org/abs/2306.10533
Autor:
Huang, Shengyu, Gojcic, Zan, Wang, Zian, Williams, Francis, Kasten, Yoni, Fidler, Sanja, Schindler, Konrad, Litany, Or
We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements, with the goal of synthesizing realistic LiDAR scans from novel viewpoints. NFL combines the rendering power of neural fields w
Externí odkaz:
http://arxiv.org/abs/2305.01643
In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters. Using imitation learning, CALM learns a representation of movement
Externí odkaz:
http://arxiv.org/abs/2305.02195