Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Siarohin, Aliaksandr"'
Autor:
Tang, Zhenggang, Zhuang, Peiye, Wang, Chaoyang, Siarohin, Aliaksandr, Kant, Yash, Schwing, Alexander, Tulyakov, Sergey, Lee, Hsin-Ying
The task of image-to-multi-view generation refers to generating novel views of an instance from a single image. Recent methods achieve this by extending text-to-image latent diffusion models to multi-view version, which contains an VAE image encoder
Externí odkaz:
http://arxiv.org/abs/2408.14016
Autor:
Bahmani, Sherwin, Skorokhodov, Ivan, Siarohin, Aliaksandr, Menapace, Willi, Qian, Guocheng, Vasilkovsky, Michael, Lee, Hsin-Ying, Wang, Chaoyang, Zou, Jiaxu, Tagliasacchi, Andrea, Lindell, David B., Tulyakov, Sergey
Modern text-to-video synthesis models demonstrate coherent, photorealistic generation of complex videos from a text description. However, most existing models lack fine-grained control over camera movement, which is critical for downstream applicatio
Externí odkaz:
http://arxiv.org/abs/2407.12781
Autor:
Fang, Yuwei, Menapace, Willi, Siarohin, Aliaksandr, Chen, Tsai-Shien, Wang, Kuan-Chien, Skorokhodov, Ivan, Neubig, Graham, Tulyakov, Sergey
Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datasets, resulting in a lack of visual grounding and restricting their ver
Externí odkaz:
http://arxiv.org/abs/2407.06304
Autor:
Haji-Ali, Moayed, Menapace, Willi, Siarohin, Aliaksandr, Balakrishnan, Guha, Tulyakov, Sergey, Ordonez, Vicente
Generating ambient sounds and effects is a challenging problem due to data scarcity and often insufficient caption quality, making it difficult to employ large-scale generative models for the task. In this work, we tackle the problem by introducing t
Externí odkaz:
http://arxiv.org/abs/2406.19388
Diffusion models have demonstrated remarkable performance in image and video synthesis. However, scaling them to high-resolution inputs is challenging and requires restructuring the diffusion pipeline into multiple independent components, limiting sc
Externí odkaz:
http://arxiv.org/abs/2406.07792
Autor:
Yu, Heng, Wang, Chaoyang, Zhuang, Peiye, Menapace, Willi, Siarohin, Aliaksandr, Cao, Junli, Jeni, Laszlo A, Tulyakov, Sergey, Lee, Hsin-Ying
Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As a result, the generated scenes are often object-centric and lack phot
Externí odkaz:
http://arxiv.org/abs/2406.07472
Autor:
Zhuang, Peiye, Han, Songfang, Wang, Chaoyang, Siarohin, Aliaksandr, Zou, Jiaxu, Vasilkovsky, Michael, Shakhrai, Vladislav, Korolev, Sergey, Tulyakov, Sergey, Lee, Hsin-Ying
We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models like LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on
Externí odkaz:
http://arxiv.org/abs/2406.05649
Autor:
Zhang, Zhixing, Li, Yanyu, Wu, Yushu, Xu, Yanwu, Kag, Anil, Skorokhodov, Ivan, Menapace, Willi, Siarohin, Aliaksandr, Cao, Junli, Metaxas, Dimitris, Tulyakov, Sergey, Ren, Jian
Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models require multiple denoising steps during sampling, resulting in high computat
Externí odkaz:
http://arxiv.org/abs/2406.04324
Autor:
Chen, Tsai-Shien, Siarohin, Aliaksandr, Menapace, Willi, Deyneka, Ekaterina, Chao, Hsiang-wei, Jeon, Byung Eun, Fang, Yuwei, Lee, Hsin-Ying, Ren, Jian, Yang, Ming-Hsuan, Tulyakov, Sergey
The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consu
Externí odkaz:
http://arxiv.org/abs/2402.19479
Autor:
Menapace, Willi, Siarohin, Aliaksandr, Skorokhodov, Ivan, Deyneka, Ekaterina, Chen, Tsai-Shien, Kag, Anil, Fang, Yuwei, Stoliar, Aleksei, Ricci, Elisa, Ren, Jian, Tulyakov, Sergey
Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances
Externí odkaz:
http://arxiv.org/abs/2402.14797