Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Gao, Quankai"'
Estimating 3D full-body avatars from AR/VR devices is essential for creating immersive experiences in AR/VR applications. This task is challenging due to the limited input from Head Mounted Devices, which capture only sparse observations from the hea
Externí odkaz:
http://arxiv.org/abs/2405.20786
Autor:
Gao, Quankai, Xu, Qiangeng, Cao, Zhe, Mildenhall, Ben, Ma, Wenchao, Chen, Le, Tang, Danhang, Neumann, Ulrich
Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly super
Externí odkaz:
http://arxiv.org/abs/2403.12365
Autor:
Chang, Di, Shi, Yichun, Gao, Quankai, Fu, Jessica, Xu, Hongyi, Song, Guoxian, Yan, Qing, Zhu, Yizhe, Yang, Xiao, Soleymani, Mohammad
In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting. Specifically, given a reference image, we aim to generate a person's new images by controlling the poses and facial expressions while kee
Externí odkaz:
http://arxiv.org/abs/2311.12052
Indoor monocular depth estimation has attracted increasing research interest. Most previous works have been focusing on methodology, primarily experimenting with NYU-Depth-V2 (NYUv2) Dataset, and only concentrated on the overall performance over the
Externí odkaz:
http://arxiv.org/abs/2309.13516
We propose Strivec, a novel neural representation that models a 3D scene as a radiance field with sparsely distributed and compactly factorized local tensor feature grids. Our approach leverages tensor decomposition, following the recent work TensoRF
Externí odkaz:
http://arxiv.org/abs/2307.13226
Autor:
Taherkhani, Fariborz, Rai, Aashish, Gao, Quankai, Srivastava, Shaunak, Chen, Xuanbai, de la Torre, Fernando, Song, Steven, Prakash, Aayush, Kim, Daeil
3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation. Existing 3D deep learning generative models (
Externí odkaz:
http://arxiv.org/abs/2208.14263
Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes. However, one main limitation with existing deep graph matchin
Externí odkaz:
http://arxiv.org/abs/2103.06643