Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields (2503.20776v2)

Abstract: Recent advancements in 2D and multimodal models have achieved remarkable success by leveraging large-scale training on extensive datasets. However, extending these achievements to enable free-form interactions and high-level semantic operations with complex 3D/4D scenes remains challenging. This difficulty stems from the limited availability of large-scale, annotated 3D/4D or multi-view datasets, which are crucial for generalizable vision and language tasks such as open-vocabulary and prompt-based segmentation, language-guided editing, and visual question answering (VQA). In this paper, we introduce Feature4X, a universal framework designed to extend any functionality from 2D vision foundation model into the 4D realm, using only monocular video input, which is widely available from user-generated content. The "X" in Feature4X represents its versatility, enabling any task through adaptable, model-conditioned 4D feature field distillation. At the core of our framework is a dynamic optimization strategy that unifies multiple model capabilities into a single representation. Additionally, to the best of our knowledge, Feature4X is the first method to distill and lift the features of video foundation models (e.g., SAM2, InternVideo2) into an explicit 4D feature field using Gaussian Splatting. Our experiments showcase novel view segment anything, geometric and appearance scene editing, and free-form VQA across all time steps, empowered by LLMs in feedback loops. These advancements broaden the scope of agentic AI applications by providing a foundation for scalable, contextually and spatiotemporally aware systems capable of immersive dynamic 4D scene interaction.

Feature4X: Bridging Monocular Videos to 4D Agentic AI

The paper under consideration introduces Feature4X, a framework designed to transform the capabilities of 2D vision foundation models into the 4D domain using monocular videos. This advancement addresses the critical challenge of extending functionalities such as segmentation, scene editing, and visual question answering (VQA) from 2D to 4D contexts—a transition that has been limited due to the scarcity of annotated 3D/4D datasets. The framework leverages dynamically distilled model-conditioned features and integrates these with two-dimensional foundational models through feedback loops involving LLMs.

Core Contributions and Methodology

Feature4X is built on several foundational advancements:

1. Dynamic Optimization Strategy: The approach employs a dynamic optimization strategy that encapsulates multiple model capabilities into a single representational framework. It aims to seamlessly distill features from video foundation models, specifically into a 4D Gaussian feature field using Gaussian splatting—an innovative method that lifts 2D features into higher-dimensional fields effectively.
2. Versatile Feature Field Representation: The paper highlights the development of a versatile 4D Gaussian feature field representation, which models dense 4D feature fields as interpolations of sparse base features, denoted as "Scaffold" features. This allows for efficient management of the increased computational load that comes with high-dimensional 4D feature representations.
3. LLM-Driven Interactivity: Feature4X is integrated with a LLM to interpret natural language prompts, adjust configuration parameters, and iteratively refine outputs. This interactive capability elevates the representation from simple perception tasks to engaging users in a perception-reasoning-action framework.

Experimental Evidence

The authors showcase Feature4X's capabilities via experiments demonstrating tasks such as novel view segmentation, 3D scene editing, and 4D VQA. These experiments reveal that the framework can adapt functionalities of 2D foundation models to interact with dynamic scenes from monocular video inputs, executing these tasks efficiently through a unified latent feature space.

Quantitative results, as depicted in their tested datasets, indicate a significant improvement in task performances with respect to previously employed baseline methods, providing competitive accuracy and a reduction in computational overhead.

Implications and Future Directions

By demonstrating that monocular videos can be leveraged to develop comprehensive 4D scene understanding and interaction frameworks, this research proposes substantial practical implications. It opens up new possibilities for utilizing readily available monocular video resources for complex 4D applications.

Theoretically, Feature4X extends the applicability of contemporary AI systems in spatial-temporal understanding, significantly impacting fields such as autonomous navigation, immersive virtual reality, and robotics. As such, the research provides a robust scaffolding for future advancements in agentic AI systems, emphasizing scalable, context-aware interactions.

Future work may explore expanding the range of foundation models integrated into the Feature4X system, refining the interactive capabilities of the LLM-driven interface, improving efficiency in handling more extensive datasets, and extending its applications into various domains beyond those tested.

In conclusion, Feature4X presents a compelling direction for AI research, particularly in bridging data limitations and extending functionality from simpler 2D models to the complex and dynamic requirements of 4D agentic AI systems.