Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Georgopoulos, Markos"'
Autor:
Polyak, Adam, Zohar, Amit, Brown, Andrew, Tjandra, Andros, Sinha, Animesh, Lee, Ann, Vyas, Apoorv, Shi, Bowen, Ma, Chih-Yao, Chuang, Ching-Yao, Yan, David, Choudhary, Dhruv, Wang, Dingkang, Sethi, Geet, Pang, Guan, Ma, Haoyu, Misra, Ishan, Hou, Ji, Wang, Jialiang, Jagadeesh, Kiran, Li, Kunpeng, Zhang, Luxin, Singh, Mannat, Williamson, Mary, Le, Matt, Yu, Matthew, Singh, Mitesh Kumar, Zhang, Peizhao, Vajda, Peter, Duval, Quentin, Girdhar, Rohit, Sumbaly, Roshan, Rambhatla, Sai Saketh, Tsai, Sam, Azadi, Samaneh, Datta, Samyak, Chen, Sanyuan, Bell, Sean, Ramaswamy, Sharadh, Sheynin, Shelly, Bhattacharya, Siddharth, Motwani, Simran, Xu, Tao, Li, Tianhe, Hou, Tingbo, Hsu, Wei-Ning, Yin, Xi, Dai, Xiaoliang, Taigman, Yaniv, Luo, Yaqiao, Liu, Yen-Cheng, Wu, Yi-Chiao, Zhao, Yue, Kirstain, Yuval, He, Zecheng, He, Zijian, Pumarola, Albert, Thabet, Ali, Sanakoyeu, Artsiom, Mallya, Arun, Guo, Baishan, Araya, Boris, Kerr, Breena, Wood, Carleigh, Liu, Ce, Peng, Cen, Vengertsev, Dimitry, Schonfeld, Edgar, Blanchard, Elliot, Juefei-Xu, Felix, Nord, Fraylie, Liang, Jeff, Hoffman, John, Kohler, Jonas, Fire, Kaolin, Sivakumar, Karthik, Chen, Lawrence, Yu, Licheng, Gao, Luya, Georgopoulos, Markos, Moritz, Rashel, Sampson, Sara K., Li, Shikai, Parmeggiani, Simone, Fine, Steve, Fowler, Tara, Petrovic, Vladan, Du, Yuming
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of
Externí odkaz:
http://arxiv.org/abs/2410.13720
Autor:
Kirschstein, Tobias, Giebenhain, Simon, Tang, Jiapeng, Georgopoulos, Markos, Nießner, Matthias
Learning 3D head priors from large 2D image collections is an important step towards high-quality 3D-aware human modeling. A core requirement is an efficient architecture that scales well to large-scale datasets and large image resolutions. Unfortuna
Externí odkaz:
http://arxiv.org/abs/2406.09377
Autor:
Oldfield, James, Georgopoulos, Markos, Chrysos, Grigorios G., Tzelepis, Christos, Panagakis, Yannis, Nicolaou, Mihalis A., Deng, Jiankang, Patras, Ioannis
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability. However, a major challenge lies in the computational c
Externí odkaz:
http://arxiv.org/abs/2402.12550
Large Language Models (LLMs) are susceptible to Jailbreaking attacks, which aim to extract harmful information by subtly modifying the attack query. As defense mechanisms evolve, directly obtaining harmful information becomes increasingly challenging
Externí odkaz:
http://arxiv.org/abs/2402.09177
Despite the remarkable capabilities of deep neural networks in image recognition, the dependence on activation functions remains a largely unexplored area and has yet to be eliminated. On the other hand, Polynomial Networks is a class of models that
Externí odkaz:
http://arxiv.org/abs/2401.17992
Autor:
Giebenhain, Simon, Kirschstein, Tobias, Georgopoulos, Markos, Rünz, Martin, Agapito, Lourdes, Nießner, Matthias
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos. To this end, we propose a latent appearance space that parameterizes a texture field on top of a neural parametric model. We
Externí odkaz:
http://arxiv.org/abs/2312.06740
Autor:
Işık, Mustafa, Rünz, Martin, Georgopoulos, Markos, Khakhulin, Taras, Starck, Jonathan, Agapito, Lourdes, Nießner, Matthias
Representing human performance at high-fidelity is an essential building block in diverse applications, such as film production, computer games or videoconferencing. To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neu
Externí odkaz:
http://arxiv.org/abs/2305.06356
Autor:
Giebenhain, Simon, Kirschstein, Tobias, Georgopoulos, Markos, Rünz, Martin, Agapito, Lourdes, Nießner, Matthias
We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we captu
Externí odkaz:
http://arxiv.org/abs/2212.02761
Generative Adversarial Networks (GANs) are the driving force behind the state-of-the-art in image generation. Despite their ability to synthesize high-resolution photo-realistic images, generating content with on-demand conditioning of different gran
Externí odkaz:
http://arxiv.org/abs/2112.12911
Autor:
Oldfield, James, Georgopoulos, Markos, Panagakis, Yannis, Nicolaou, Mihalis A., Patras, Ioannis
This paper addresses the problem of finding interpretable directions in the latent space of pre-trained Generative Adversarial Networks (GANs) to facilitate controllable image synthesis. Such interpretable directions correspond to transformations tha
Externí odkaz:
http://arxiv.org/abs/2111.11736