Inspired by the effectiveness of 3D Gaussian Splatting (3DGS) in reconstructing detailed 3D scenes within multi-view setups and the emergence of large 2D human foundation models, we introduce Arc2Avatar, the first SDS-based method utilizing a human face foundation model as guidance with just a single image as input. To achieve that, we extend such a model for diverse-view human head generation by fine-tuning on synthetic data and modifying its conditioning. Our avatars maintain a dense correspondence with a human face mesh template, allowing blendshape-based expression generation. This is achieved through a modified 3DGS approach, connectivity regularizers, and a strategic initialization tailored for our task. Additionally, we propose an optional efficient SDS-based correction step to refine the blendshape expressions, enhancing realism and diversity. Experiments demonstrate that Arc2Avatar achieves state-of-the-art realism and identity preservation, effectively addressing color issues by allowing the use of very low guidance, enabled by our strong identity prior and initialization strategy, without compromising detail.
受益于 3D 高斯点绘制（3D Gaussian Splatting, 3DGS）在多视角设置中重建精细 3D 场景的能力，以及大规模 2D 人体基础模型的兴起，我们提出了 Arc2Avatar，这是首个利用基于 SDS 的方法并以人脸基础模型为引导的技术，仅需单张输入图像即可生成结果。为实现这一目标，我们通过在合成数据上进行微调和修改条件输入，将人脸基础模型扩展用于多视角人头生成。 生成的虚拟人头与一个人脸网格模板保持密集对应关系，从而支持基于混合变形（blendshape）的表情生成。这一过程通过改进的 3DGS 方法、连通性正则器以及为任务量身定制的初始化策略得以实现。此外，我们提出了一种可选的高效 SDS 校正步骤，用于细化混合变形表情，从而进一步提升现实感和多样性。 实验结果表明，Arc2Avatar 在现实感和身份保留方面达到了最先进水平，通过我们的强身份先验和初始化策略，能够在保持细节的同时有效解决颜色问题，仅需使用非常低的引导强度即可实现。这项技术显著提升了生成结果的真实性和多样性，推动了单张图像驱动的虚拟人头生成的技术前沿。