Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Xu, Dejia"'
Using the latent diffusion model has proven effective in developing novel 3D generation techniques. To harness the latent diffusion model, a key challenge is designing a high-fidelity and efficient representation that links the latent space and the 3
Externí odkaz:
http://arxiv.org/abs/2408.13055
Autor:
Li, Renjie, Pan, Panwang, Yang, Bangbang, Xu, Dejia, Zhou, Shijie, Zhang, Xuanyang, Li, Zeming, Kadambi, Achuta, Wang, Zhangyang, Tu, Zhengzhong, Fan, Zhiwen
The blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on dynamic o
Externí odkaz:
http://arxiv.org/abs/2406.13527
Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often do not offer precise control over camera poses for video generation, l
Externí odkaz:
http://arxiv.org/abs/2406.02509
Autor:
Wang, Zhiqiang, Xu, Dejia, Khan, Rana Muhammad Shahroz, Lin, Yanbin, Fan, Zhiwen, Zhu, Xingquan
Image geolocation is a critical task in various image-understanding applications. However, existing methods often fail when analyzing challenging, in-the-wild images. Inspired by the exceptional background knowledge of multimodal language models, we
Externí odkaz:
http://arxiv.org/abs/2405.20363
Autor:
Liang, Hanwen, Yin, Yuyang, Xu, Dejia, Liang, Hanxue, Wang, Zhangyang, Plataniotis, Konstantinos N., Zhao, Yao, Wei, Yunchao
The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rely on multiple image or video diffusion models, utilizing score distill
Externí odkaz:
http://arxiv.org/abs/2405.16645
Autor:
D'Incà, Moreno, Peruzzo, Elia, Mancini, Massimiliano, Xu, Dejia, Goel, Vidit, Xu, Xingqian, Wang, Zhangyang, Shi, Humphrey, Sebe, Nicu
Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments, it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any
Externí odkaz:
http://arxiv.org/abs/2404.07990
Autor:
Zhou, Shijie, Fan, Zhiwen, Xu, Dejia, Chang, Haoran, Chari, Pradyumna, Bharadwaj, Tejas, You, Suya, Wang, Zhangyang, Kadambi, Achuta
The increasing demand for virtual reality applications has highlighted the significance of crafting immersive 3D assets. We present a text-to-3D 360$^{\circ}$ scene generation pipeline that facilitates the creation of comprehensive 360$^{\circ}$ scen
Externí odkaz:
http://arxiv.org/abs/2404.06903
Autor:
Xu, Dejia, Liang, Hanwen, Bhatt, Neel P., Hu, Hezhen, Liang, Hanxue, Plataniotis, Konstantinos N., Wang, Zhangyang
Recent advancements in diffusion models for 2D and 3D content creation have sparked a surge of interest in generating 4D content. However, the scarcity of 3D scene datasets constrains current methodologies to primarily object-centric generation. To o
Externí odkaz:
http://arxiv.org/abs/2403.16993
Autor:
Xu, Dejia, Yuan, Ye, Mardani, Morteza, Liu, Sifei, Song, Jiaming, Wang, Zhangyang, Vahdat, Arash
Given the growing need for automatic 3D content creation pipelines, various 3D representations have been studied to generate 3D objects from a single image. Due to its superior rendering efficiency, 3D Gaussian splatting-based models have recently ex
Externí odkaz:
http://arxiv.org/abs/2401.04099
Autor:
Peruzzo, Elia, Goel, Vidit, Xu, Dejia, Xu, Xingqian, Jiang, Yifan, Wang, Zhangyang, Shi, Humphrey, Sebe, Nicu
Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models. However, most of these methods holistically edit the frame using the text, exploiting the prior given by foundation diffusi
Externí odkaz:
http://arxiv.org/abs/2401.02473