4D Scene Understanding: Language-Based Navigation in Dynamic Environments Thanks to 4D LangSplat

Interacting with three-dimensional, dynamic environments using natural language is a key topic in current AI research. Applications range from robotics and autonomous navigation to immersive virtual worlds. A promising approach in this area is the representation of scenes as so-called "language fields," which directly link semantic information with spatial coordinates. Building upon the success of 3D methods like LangSplat, which effectively link static scenes with language, 4D LangSplat represents an innovative extension for dynamic, time-dependent scenes.

Previous approaches, such as LangSplat, utilize pre-trained image-text models like CLIP to extract semantic features from images and connect them with 3D Gaussian representations. This allows querying scenes based on text descriptions. However, the limitation to static images poses a challenge for application in dynamic environments, as temporal changes and movements are not considered.

4D LangSplat addresses this challenge by integrating multimodal large language models (MLLMs). Instead of relying on visual features, 4D LangSplat leverages the ability of MLLMs to generate detailed and temporally consistent descriptions of objects in videos. Through a specially developed, object-based video prompting procedure, which combines visual and textual prompts, the MLLMs are encouraged to create high-quality descriptions for each object throughout the entire video sequence.

These descriptions are subsequently converted by a large language model into so-called sentence embeddings, which serve as pixel-accurate, object-specific features. Through this approach, a bridge is built between the visual world and the linguistic description, enabling open text queries. The use of sentence embeddings allows the search for objects based on their semantic meaning, regardless of their visual representation.

Another important aspect of 4D LangSplat is the consideration of continuous state changes of objects in dynamic scenes. For this purpose, a so-called "Status Deformable Network" is used, which models the smooth transitions between different object states. This enables a more precise and realistic representation of the temporal evolution within the scene.

The results of 4D LangSplat on various benchmarks show a significant improvement over previous approaches, both for time-dependent and time-independent queries. The method enables efficient and precise interaction with dynamic 4D scenes and opens up new possibilities for applications in areas such as robotics, virtual reality, and human-computer interaction.

The development of 4D LangSplat underscores the potential of multimodal large language models for the processing and understanding of complex, dynamic scenes. The combination of visual information with the language capabilities of MLLMs opens new avenues for interaction with virtual and real environments and contributes to closing the gap between human language and machine understanding.

Bibliography: Qin, M., et al. "LangSplat: 3D Language Gaussian Splatting." *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2024. Li, W., et al. "4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models." *arXiv preprint arXiv:2503.10437*. 2025. Herter, J., et al. "4D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes." *arXiv preprint arXiv:2410.10719*. 2024.