Modeling the time-varying 3D appearance of plants during their growth poses unique challenges: unlike many dynamic scenes, plants generate new geometry over time as they expand, branch, and differentiate. Recent motion modeling techniques are ill-suited to this problem setting. For example, deformation fields cannot introduce new geometry, and 4D Gaussian splatting constrains motion to a linear trajectory in space and time and cannot track the same set of Gaussians over time. Here, we introduce a 3D Gaussian flow field representation that models plant growth as a time-varying derivative over Gaussian parameters---position, scale, orientation, color, and opacity---enabling nonlinear and continuous-time growth dynamics. To initialize a sufficient set of Gaussian primitives, we reconstruct the mature plant and learn a process of reverse growth, effectively simulating the plant’s developmental history in reverse. Our approach achieves superior image quality and geometric accuracy compared to prior methods on multi-view timelapse datasets of plant growth, providing a new approach 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.