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.

本研究解决了如何从单张图像生成具有表现力的3D头像的难题。提出的Arc2Avatar方法利用人脸基础模型进行指导，结合了精细调优和条件修改技术，成功实现了多视角人头的生成。实验结果表明，该方法在真实性和身份保持方面达到最先进水平，并有效改善了色彩问题，展示了其在3D头像生成领域的重大影响。

Arc2Avatar：通过ID引导从单张图像生成生动的3D头像