mlsanityData to 7 Oct 2026

PEGASUS: Personalized Generative 3D Avatars with Composable Attributes

arXiv 2402.10636 ↗First posted 16 Feb 2024cs.CVcited by 7

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Abstract (arXiv)

We present PEGASUS, a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selectively alter the facial attributes (e.g., hair or nose) while preserving the identity. Our approach consists of two stages: synthetic database generation and constructing a personalized generative avatar. We generate a synthetic video collection of the target identity with varying facial attributes, where the videos are synthesized by borrowing the attributes from monocular videos of diverse identities. Then, we build a person-specific generative 3D avatar that can modify its attributes continuously while preserving its identity. Through extensive experiments, we demonstrate that our method of generating a synthetic database and creating a 3D generative avatar is the most effective in preserving identity while achieving high realism. Subsequently, we introduce a zero-shot approach to achieve the same goal of generative modeling more efficiently by leveraging a previously constructed personalized generative model.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 62 of its 83 references that are in the map. First posted on arXiv 16 Feb 2024.

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62 of 83 references.

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.