Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile
  Gaussian Feature Fields
Authors: name: Shijie Zhou, name: Hui Ren, name: Yijia Weng, name: Shuwang Zhang, name: Zhen Wang, name: Dejia Xu, name: Zhiwen Fan, name: Suya You, name: Zhangyang Wang, name: Leonidas Guibas, name: Achuta Kadambi

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.