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pro vyhledávání: '"Valevski, Dani"'
We present GameNGen, the first game engine powered entirely by a neural model that enables real-time interaction with a complex environment over long trajectories at high quality. GameNGen can interactively simulate the classic game DOOM at over 20 f
Externí odkaz:
http://arxiv.org/abs/2408.14837
Text-to-image diffusion models achieved a remarkable leap in capabilities over the last few years, enabling high-quality and diverse synthesis of images from a textual prompt. However, even the most advanced models often struggle to precisely follow
Externí odkaz:
http://arxiv.org/abs/2310.16656
We present Face0, a novel way to instantaneously condition a text-to-image generation model on a face, in sample time, without any optimization procedures such as fine-tuning or inversions. We augment a dataset of annotated images with embeddings of
Externí odkaz:
http://arxiv.org/abs/2306.06638
Autor:
Molad, Eyal, Horwitz, Eliahu, Valevski, Dani, Acha, Alex Rav, Matias, Yossi, Pritch, Yael, Leviathan, Yaniv, Hoshen, Yedid
Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the first diffusi
Externí odkaz:
http://arxiv.org/abs/2302.01329
Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Te
Externí odkaz:
http://arxiv.org/abs/2210.09477
Akademický článek
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