Computer Science > Computer Vision and Pattern Recognition
arXiv:2402.16607 (cs)
[Submitted on 26 Feb 2024 (v1), last revised 19 Mar 2024 (this version, v2)]
Title:GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos
Authors:Xinqi Liu,Chenming Wu,Jialun Liu,Xing Liu,Jinbo Wu,Chen Zhao,Haocheng Feng,Errui Ding,Jingdong Wang
View a PDF of the paper titled GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos, by Xinqi Liu and 8 other authors
View PDFHTML (experimental)Abstract:In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA). Our innovation lies in addressing the intricate challenges of delivering high-fidelity human body reconstructions and aligning 3D Gaussians with human skin surfaces accurately. The key contributions of this paper are twofold. Firstly, we introduce a pose refinement technique to improve hand and foot pose accuracy by aligning normal maps and silhouettes. Precise pose is crucial for correct shape and appearance reconstruction. Secondly, we address the problems of unbalanced aggregation and initialization bias that previously diminished the quality of 3D Gaussian avatars, through a novel surface-guided re-initialization method that ensures accurate alignment of 3D Gaussian points with avatar surfaces. Experimental results demonstrate that our proposed method achieves high-fidelity and vivid 3D Gaussian avatar reconstruction. Extensive experimental analyses validate the performance qualitatively and quantitatively, demonstrating that it achieves state-of-the-art performance in photo-realistic novel view synthesis while offering fine-grained control over the human body and hand pose. Project page:this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2402.16607 [cs.CV] |
| (orarXiv:2402.16607v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2402.16607 arXiv-issued DOI via DataCite |
Submission history
From: Xinqi Liu [view email][v1] Mon, 26 Feb 2024 14:40:15 UTC (21,625 KB)
[v2] Tue, 19 Mar 2024 08:58:17 UTC (11,743 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos, by Xinqi Liu and 8 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.