Generating high-fidelity 3D head avatars from a single image is challenging, as current methods lack fine-grained, intuitive control over expressions via text. This paper proposes SIE3D, a framework that generates expressive 3D avatars from a single image and descriptive text. SIE3D fuses identity features from the image with semantic embedding from text through a novel conditioning scheme, enabling detailed control. To ensure generated expressions accurately match the text, it introduces an innovative perceptual expression loss function. This loss uses a pre-trained expression classifier to regularize the generation process, guaranteeing expression accuracy. Extensive experiments show SIE3D significantly improves controllability and realism, outperforming competitive methods in identity preservation and expression fidelity on a single consumer-grade GPU. Project page: https://huang-zhiqi.github.io/SIE3D/
@article{arxiv.2509.24004,
title = {SIE3D: Single-Image Expressive 3D Avatar Generation via Semantic Embedding and Perceptual Expression Loss},
author = {Zhiqi Huang and Dulongkai Cui and Jinglu Hu},
journal= {arXiv preprint arXiv:2509.24004},
year = {2026}
}
Comments
Published in ICASSP 2026. 5 pages, 3 figures. Project page: https://huang-zhiqi.github.io/SIE3D/