Authenticating Matryoshka Nesting Dolls via MML-LLM-Zero-shot 3D Reconstruction

This work presents a multimodal machine learning (MML) pipeline with zero-shot 3D completion for the digital preservation and authentication of Matryoshka nesting dolls (MND). A private collection is digitized as this novel multimodal dataset centered on turntable videos, augmented with single and group images and auxiliary physical and textual cues. A text modality is produced using Qwen3VL captions to enable video-text fusion and semantic motif analysis. A unimodal 2D baseline is established for fine-grained 8-way style recognition and a 3-way authenticity task, and is compared against multimodal configurations that incorporate learned text embeddings. To incorporate geometry as direct evidence, the pipeline integrates a silhouette-to-skeleton branch based on the Blum medial axis (BMA) and a convolutional autoencoder (CA) that reconstructs dense silhouettes from sparse skeletons, yielding a compact representation suitable for downstream 3D reasoning. The 3D pipeline is implemented along two complementary branches: zero-shot completion with a pretrained 3D prior (Hunyuan3D) and mesh-oriented skeletonization via a custom BMA procedure. Late fusion combines geometric and textual signals to improve decision confidence beyond appearance-only models. The framework supports authentication decisions with explicit geometric and semantic evidence and is transferable to other cultural artifacts. Potential applications include AR/VR, education, gaming, and assistive technologies. The code for this project is available upon request.