Abstract: 3D visual language multi-modal modeling plays an important role in actual human-computer interaction. However, the inaccessibility of large scale 3D-language pairs restricts their applicability in real-world scenarios. In this paper, we aim to handle a real-time multi-task for 6-DoF pose tracking of unknown objects, leveraging 3D-language pre-training scheme from a series of 3D point cloud video streams, while simultaneously performing 3D shape reconstruction in current observation. To this end, we present a generic $\underline{L}$anguage-to-$\underline{4D}$ modeling paradigm termed L4D-Track, that tackles zero-shot 6-DoF $\underline{Track}$ing and shape reconstruction by learning pairwise implicit 3D representation and multi-level multi-modal alignment. Our method constitutes two core parts. 1) Pairwise Implicit 3D Space Representation, that establishes spatial-temporal to language coherence descriptions across continuous 3D point cloud video. 2) Language-to-4D Association and Contrastive Alignment, enables multi-modality semantic connections between 3D point cloud video and language. Our method trained exclusively on public NOCS-REAL275 dataset, achieves promising results on both two publicly benchmarks. This not only shows powerful generalization performance, but also proves its remarkable capability in zero-shot inference. The project is released at https://s-jingtao.github.io/L4D-Track/.