3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation

The SSL-MedSAM2 framework has been introduced as a semi-supervised learning approach for medical image segmentation, leveraging few-shot learning techniques from the Segment Anything Model 2 (SAM2) to generate and refine pseudo labels. This innovation aims to address the challenges posed by the need for extensive annotated datasets in traditional fully-supervised models, which are often impractical in clinical settings.

A new algorithm has been introduced to distill structure-preserving motion from an autoregressive video tracking model (SAM2) into a bidirectional video diffusion model (CogVideoX), addressing challenges in generating realistic motion for articulated and deformable objects. This advancement aims to enhance fidelity in video generation, particularly for complex subjects like humans and animals.

MultiMotion has been introduced as a novel framework for multi-object video motion transfer, utilizing a Maskaware Attention Motion Flow (AMF) to disentangle and control motion features within the Diffusion Transformer (DiT) architecture. This innovation addresses challenges related to motion entanglement and object-level control, enhancing the capabilities of video generation.