https://headgap.github.io/\n","updatedAt":"2024-08-13T03:06:16.467Z","author":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","fullname":"AK","name":"akhaliq","type":"user","isPro":false,"isHf":true,"isHfAdmin":false,"isMod":false,"followerCount":9179,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.2665840983390808},"editors":["akhaliq"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg"],"reactions":[{"reaction":"๐ค","users":["zhaohu2","walsvid"],"count":2}],"isReport":false}},{"id":"66bc0927bfdf2873fc5d3bbd","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2024-08-14T01:32:23.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [FAGhead: Fully Animate Gaussian Head from Monocular Videos](https://huggingface.co/papers/2406.19070) (2024)\n* [PAV: Personalized Head Avatar from Unstructured Video Collection](https://huggingface.co/papers/2407.21047) (2024)\n* [3D Gaussian Parametric Head Model](https://huggingface.co/papers/2407.15070) (2024)\n* [Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360{\\deg}](https://huggingface.co/papers/2408.00296) (2024)\n* [Portrait3D: 3D Head Generation from Single In-the-wild Portrait Image](https://huggingface.co/papers/2406.16710) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
\nThe following papers were recommended by the Semantic Scholar API
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- FAGhead: Fully Animate Gaussian Head from Monocular Videos (2024) \n
- PAV: Personalized Head Avatar from Unstructured Video Collection (2024) \n
- 3D Gaussian Parametric Head Model (2024) \n
- Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360{\\deg} (2024) \n
- Portrait3D: 3D Head Generation from Single In-the-wild Portrait Image (2024) \n
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Papers
arxiv:2408.06019
HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors
Published on Aug 12, 2024
Abstract
A framework for creating 3D photorealistic and animatable head avatars using prior learning and few-shot personalization with Gaussian Splatting and dynamic modeling.
AI-generated summary
In this paper, we present a novel 3D head avatar creation approach capable of generalizing from few-shot in-the-wild data with high-fidelity and animatable robustness. Given the underconstrained nature of this problem, incorporating prior knowledge is essential. Therefore, we propose a framework comprising prior learning and avatar creation phases. The prior learning phase leverages 3D head priors derived from a large-scale multi-view dynamic dataset, and the avatar creation phase applies these priors for few-shot personalization. Our approach effectively captures these priors by utilizing a Gaussian Splatting-based auto-decoder network with part-based dynamic modeling. Our method employs identity-shared encoding with personalized latent codes for individual identities to learn the attributes of Gaussian primitives. During the avatar creation phase, we achieve fast head avatar personalization by leveraging inversion and fine-tuning strategies. Extensive experiments demonstrate that our model effectively exploits head priors and successfully generalizes them to few-shot personalization, achieving photo-realistic rendering quality, multi-view consistency, and stable animation.
Community
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- FAGhead: Fully Animate Gaussian Head from Monocular Videos (2024)
- PAV: Personalized Head Avatar from Unstructured Video Collection (2024)
- 3D Gaussian Parametric Head Model (2024)
- Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360{\deg} (2024)
- Portrait3D: 3D Head Generation from Single In-the-wild Portrait Image (2024)
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