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pro vyhledávání: '"Bar-Tal A"'
Autor:
Chai, Wenhao, Song, Enxin, Du, Yilun, Meng, Chenlin, Madhavan, Vashisht, Bar-Tal, Omer, Hwang, Jeng-Neng, Xie, Saining, Manning, Christopher D.
Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation. In this paper, we propose AuroraCap, a video captioner based on a lar
Externí odkaz:
http://arxiv.org/abs/2410.03051
Autor:
Bar-Tal, Omer, Chefer, Hila, Tov, Omer, Herrmann, Charles, Paiss, Roni, Zada, Shiran, Ephrat, Ariel, Hur, Junhwa, Liu, Guanghui, Raj, Amit, Li, Yuanzhen, Rubinstein, Michael, Michaeli, Tomer, Wang, Oliver, Sun, Deqing, Dekel, Tali, Mosseri, Inbar
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that gen
Externí odkaz:
http://arxiv.org/abs/2401.12945
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
We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their sem
Externí odkaz:
http://arxiv.org/abs/2311.12193
The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a
Externí odkaz:
http://arxiv.org/abs/2307.10373
Autor:
Bar, Tal, Meerson, Baruch
Publikováno v:
J. Stat. Mech. (2023) 093301
Geometrical optics provides an instructive insight into Brownian motion, ``pushed" into a large-deviations regime by imposed constraints. Here we extend geometrical optics of Brownian motion by accounting for diffusion inhomogeneity in space. We cons
Externí odkaz:
http://arxiv.org/abs/2305.05942
Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently
Externí odkaz:
http://arxiv.org/abs/2302.08113
Publikováno v:
Journal of Elasticity (2023)
The geometry and interactions between the constituents of a liquid crystal, which are responsible for inducing the partial order in the fluid, may locally favor an attempted phase that could not be realized in $\mathbb{R}^3$. While states that are in
Externí odkaz:
http://arxiv.org/abs/2211.08598
We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment th
Externí odkaz:
http://arxiv.org/abs/2204.02491
We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their sem
Externí odkaz:
http://arxiv.org/abs/2201.00424