Modeling the time-varying 3D appearance of plants during growth poses unique challenges: unlike most dynamic scenes, plants continuously generate new geometry as they expand, branch, and differentiate. Existing dynamic scene representations are ill-suited to this setting: deformation fields provide insufficient constraints to yield physically plausible scene dynamics, and 4D Gaussian splatting represents the same physical structures with different Gaussian primitives at different times, breaking temporal consistency. We introduce GrowFlow, a dynamic representation that couples 3D Gaussian primitives with a neural ordinary differential equation to model plant growth as a continuous flow field over geometric parameters (position, scale, and orientation). Our representation enables consistent appearance rendering and models nonlinear, continuous-time growth dynamics with full temporal correspondences for every primitive. To initialize a sufficient set of Gaussian primitives, we first reconstruct the mature plant and then learn a reverse-growth process, effectively simulating the plant's developmental history in reverse. GrowFlow achieves superior image quality and geometric coherence compared to prior methods on a new, multi-view timelapse dataset of plant growth, and provides the first temporally coherent representation for appearance modeling of growing 3D structures.

a) Our method first optimizes a set of 3D Gaussians on the fully-grown plant. b) Using the optimized 3D Gaussians from the fully-grown plant, we progressively train the dynamics model to learn the state of the plant at each timestep. After each reconstructed timestep, we cache the Gaussians for that timestep and use them as initial conditions to optimize for the next timestep. c) During the global optimization step, we randomly sample a timestep t_k and integrate to t_k+1, leveraging the cached Gaussians from the boundary reconstruction step as initial conditions. We then optimize the dynamics model to enforce consistency between rendered and captured measurements.