Zobrazeno 1 - 10
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pro vyhledávání: '"Xu, Jianjin"'
Autor:
Kwon, Youngjoong, Fang, Baole, Lu, Yixing, Dong, Haoye, Zhang, Cheng, Carrasco, Francisco Vicente, Mosella-Montoro, Albert, Xu, Jianjin, Takagi, Shingo, Kim, Daeil, Prakash, Aayush, De la Torre, Fernando
Recent progress in neural rendering has brought forth pioneering methods, such as NeRF and Gaussian Splatting, which revolutionize view rendering across various domains like AR/VR, gaming, and content creation. While these methods excel at interpolat
Externí odkaz:
http://arxiv.org/abs/2407.12777
Autor:
Xu, Jianjin, Motamed, Saman, Vaddamanu, Praneetha, Wu, Chen Henry, Haene, Christian, Bazin, Jean-Charles, de la Torre, Fernando
Face inpainting is important in various applications, such as photo restoration, image editing, and virtual reality. Despite the significant advances in face generative models, ensuring that a person's unique facial identity is maintained during the
Externí odkaz:
http://arxiv.org/abs/2312.03556
Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. However, current generative models for face inpainting often fail to pre
Externí odkaz:
http://arxiv.org/abs/2304.06107
Recently, unsupervised learning has made impressive progress on various tasks. Despite the dominance of discriminative models, increasing attention is drawn to representations learned by generative models and in particular, Generative Adversarial Net
Externí odkaz:
http://arxiv.org/abs/2211.16710
Large-scale pre-trained language models have achieved great success on natural language generation tasks. However, it is difficult to control the pre-trained language models to generate sentences with the desired attribute such as topic and sentiment
Externí odkaz:
http://arxiv.org/abs/2206.05519
Autor:
Xu, Jianjin, Zheng, Changxi
Generative Adversarial Networks (GANs) are able to generate high-quality images, but it remains difficult to explicitly specify the semantics of synthesized images. In this work, we aim to better understand the semantic representation of GANs, and th
Externí odkaz:
http://arxiv.org/abs/2104.00487
Neural style transfer models have been used to stylize an ordinary video to specific styles. To ensure temporal inconsistency between the frames of the stylized video, a common approach is to estimate the optic flow of the pixels in the original vide
Externí odkaz:
http://arxiv.org/abs/2102.05822
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