Instant Facial Gaussians Translator for Relightable and Interactable Facial Rendering
<|reference_start|>Instant Facial Gaussians Translator for Relightable and Interactable Facial Rendering: We propose GauFace, a novel Gaussian Splatting representation, tailored for efficient animation and rendering of physically-based facial assets. Leveraging strong geometric priors and constrained optimization, GauFace ensures a neat and structured Gaussian representation, delivering high fidelity and real-time facial interaction of 30fps@1440p on a Snapdragon 8 Gen 2 mobile platform.   Then, we introduce TransGS, a diffusion transformer that instantly translates physically-based facial assets into the corresponding GauFace representations. Specifically, we adopt a patch-based pipeline to handle the vast number of Gaussians effectively. We also introduce a novel pixel-aligned sampling scheme with UV positional encoding to ensure the throughput and rendering quality of GauFace assets generated by our TransGS. Once trained, TransGS can instantly translate facial assets with lighting conditions to GauFace representation, With the rich conditioning modalities, it also enables editing and animation capabilities reminiscent of traditional CG pipelines.   We conduct extensive evaluations and user studies, compared to traditional offline and online renderers, as well as recent neural rendering methods, which demonstrate the superior performance of our approach for facial asset rendering. We also showcase diverse immersive applications of facial assets using our TransGS approach and GauFace representation, across various platforms like PCs, phones and even VR headsets.<|reference_end|>
@article{qin2024instant,
    title={Instant Facial Gaussians Translator for Relightable and Interactable
  Facial Rendering},
    author={Dafei Qin, Hongyang Lin, Qixuan Zhang, Kaichun Qiao, Longwen Zhang,
  Zijun Zhao, Jun Saito, Jingyi Yu, Lan Xu, Taku Komura},
    journal={arXiv preprint arXiv:2409.07441},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07441},
    primaryClass={cs.GR}
}