Seung‐Eun Lee

SeungJun Moon, Hah Min Lew, Seungeun Lee et al.

Despite recent progress in 3D head avatar generation, balancing identity preservation, i.e., reconstruction, with novel poses and expressions, i.e., animation, remains a challenge. Existing methods struggle to adapt Gaussians to varying geometrical deviations across facial regions, resulting in suboptimal quality. To address this, we propose GeoAvatar, a framework for adaptive geometrical Gaussian Splatting. GeoAvatar leverages Adaptive Pre-allocation Stage (APS), an unsupervised method that segments Gaussians into rigid and flexible sets for adaptive offset regularization. Then, based on mouth anatomy and dynamics, we introduce a novel mouth structure and the part-wise deformation strategy to enhance the animation fidelity of the mouth. Finally, we propose a regularization loss for precise rigging between Gaussians and 3DMM faces. Moreover, we release DynamicFace, a video dataset with highly expressive facial motions. Extensive experiments show the superiority of GeoAvatar compared to state-of-the-art methods in reconstruction and novel animation scenarios.

Seungeun Lee, Il-Youp Kwak, Kihwan Lee et al.

Recent advancements in deep learning for tabular data have shown promise, but challenges remain in achieving interpretable and lightweight models. This paper introduces Table2Image, a novel framework that transforms tabular data into realistic and diverse image representations, enabling deep learning methods to achieve competitive classification performance. To address multicollinearity in tabular data, we propose a variance inflation factor (VIF) initialization, which enhances model stability and robustness by incorporating statistical feature relationships. Additionally, we present an interpretability framework that integrates insights from both the original tabular data and its transformed image representations, by leveraging Shapley additive explanations (SHAP) and methods to minimize distributional discrepancies. Experiments on benchmark datasets demonstrate the efficacy of our approach, achieving superior accuracy, area under the curve, and interpretability compared to recent leading deep learning models. Our lightweight method provides a scalable and reliable solution for tabular data classification.