SOTA method struggles on our TeethWild dataset.

Accurate semantic segmentation of 3D dental models is essential for digital dentistry applications such as orthodontics and dental implants. However, due to complex tooth arrangements and similarities in shape among adjacent teeth, existing methods struggle with accurate segmentation because they often focus on local geometry while neglecting global contextual information. To address this, we propose TCATSeg, a novel framework that combines local geometric features with global semantic context. We introduce a set of sparse yet physically meaningful superpoints to capture global semantic relationships and enhance segmentation accuracy. Additionally, we present a new dataset of 400 dental models, including pre-orthodontic samples, to evaluate the generalization of our method. Extensive experiments demonstrate that TCATSeg outperforms state-of-the-art approaches.

In our conditional diffusion model, we use the given sequence of object motion, object shape and the SMPL-X identity as conditions. After specially designed positional encodings, these embedded conditions are inputted into a transformer-encoder-based condition encoder. Then, a transformer decoder as denoising network predicts a sequence of clean whole-body pose of SMPL-X as well as the wrist joints translations relative to the object centroid. During the inference stage, we reconstruct the SMPL-X pose sequence into a human mesh sequence. Based on carefully designed guidance functions, we control and optimize our predicted results for more stable hand grasping, less penetration and better foot-floor contact through reconstruction guidance strategy.

Q. He, W. Qu, J. Dai, C. Lei, S. Wang, F. Zuo, Y. Wang, Y. Liang, X. Deng, C. Ma, Y.-J. Liu, H. Wang TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation. arXiv:2603.16620, 2026. [Paper]     [Bibtex]     [Code]