FreeGaussian: Guidance-free Controllable 3D Gaussian Splats with Flow Derivatives

ICLR 2025 Conference Submission342 Authors

Keywords: 3D Gaussian Splatting, Controllable View Synthesis

TL;DR: Derive Gaussian dynamics from optical flow and camera motion; Guidance-free Controllable 3DGS without any annotations

Abstract: Reconstructing controllable Gaussian splats from monocular video is a challenging task due to its inherently insufficient constraints. Widely adopted approaches supervise complex interactions with additional masks and control signal annotations, limiting their real-world applications. In this paper, we propose an annotation guidance-free method, dubbed FreeGaussian, that mathematically derives dynamic Gaussian motion from optical flow and camera motion using novel dynamic Gaussian constraints. By establishing a connection between 2D flows and 3D Gaussian dynamic control, our method enables self-supervised optimization and continuity of dynamic Gaussian motions from flow priors. Furthermore, we introduce a 3D spherical vector controlling scheme, which represents the state with a 3D Gaussian trajectory, thereby eliminating the need for complex 1D control signal calculations and simplifying controllable Gaussian modeling. Quantitative and qualitative evaluations on extensive experiments demonstrate the state-of-the-art visual performance and control capability of our method.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 342