发表机构
German Research Center for Artificial Intelligence (DFKI); RPTU（德国人工智能研究中心; 莱茵兰-普法尔茨技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有上半身头像动画方法的不足，提出基于动态高斯的 MVFGA 多视图流水线，结合参数模型与 3D 高斯实现高保真渲染，引入对应数据集，效果优于基线。
AI 中文摘要
创建具有逼真上半身运动的 photorealistic 3D 数字人头像仍具有挑战性。现有方法要么聚焦于头部而忽略手势，要么重建全身但无法保留细粒度的面部保真度和手部姿态准确性。因此，当前方法难以捕捉对自然人类交流至关重要的面部表情和手势的微妙动态。尽管基于全身参数模型的方法能够从单目或多视图输入中重建头像，但它们通常缺乏准确的面部动画和详细的手部关节。为解决这些限制，我们提出 MVFGA，一种用于生成逼真上半身头像的新型多视图一致流水线。我们的方法分别对面部和手部进行建模，并将它们与参数化上半身网格模型融合，从而能够捕捉细粒度的面部表情和手部姿态，以实现准确的上半身头像重建。随后，我们将 3D 高斯 splat 到获得的网格上，实现从 novel viewpoints 对动态头像的高质量渲染。此外，我们引入 MVFGA-MoCap，一种多视图上半身动作捕捉数据集，包含受控面部表情序列、多样手势和自由形式交流。实验表明，MVFGA 生成具有高保真面部表情和手部运动的视觉逼真头像，在上半身头像动画方面优于基线方法。项目页面：this https URL
英文摘要
Creating photorealistic 3D human avatars with realistic upper-body motion remains challenging. Existing approaches either focus on the head and overlook hand gestures, or reconstruct the full body but fail to preserve fine-grained facial fidelity and hand pose accuracy. As a result, current methods struggle to capture the subtle dynamics of facial expressions and hand gestures that are crucial for natural human communication. While methods based on full-body parametric models enable avatar reconstruction from monocular or multi-view inputs, they often lack accurate facial animation and detailed hand articulation. To address these limitations, we propose MVFGA, a novel multi-view-consistent pipeline for generating realistic upper-body avatars. Our approach models the face and hands separately and fuses them with a parametric upper-body mesh model, enabling the capture of fine-grained facial expressions and hand poses for accurate upper-body avatar reconstruction. We then splat 3D Gaussians onto the obtained mesh, enabling high-quality rendering of dynamic avatars from novel viewpoints. Furthermore, we introduce MVFGA-MoCap, a multi-view upper-body motion capture dataset featuring controlled facial expression sequences, diverse hand gestures, and free-form communication. Experiments show that MVFGA generates visually realistic avatars with high-fidelity facial expressions and hand motions, outperforming baselines for upper-body avatar animation. Project page: https://dfki-av.github.io/MVFGA/
CommentsAccepted at SCA 2026
Journal refComputer Graphics Forum, 45(8), 2026
