TC4D: Trajectory-Conditioned Text-to-4D Generation
Authors: name: Sherwin Bahmani, name: Xian Liu, name: Yifan Wang, name: Ivan Skorokhodov, name: Victor Rong, name: Ziwei Liu, name: Xihui Liu, name: Jeong Joon Park, name: Sergey Tulyakov, name: Gordon Wetzstein, name: Andrea Tagliasacchi, name: David B. Lindell

Recent techniques for text-to-4D generation synthesize dynamic 3D scenes
using supervision from pre-trained text-to-video models. However, existing
representations for motion, such as deformation models or time-dependent neural
representations, are limited in the amount of motion they can generate-they
cannot synthesize motion extending far beyond the bounding box used for volume
rendering. The lack of a more flexible motion model contributes to the gap in
realism between 4D generation methods and recent, near-photorealistic video
generation models. Here, we propose TC4D: trajectory-conditioned text-to-4D
generation, which factors motion into global and local components. We represent
the global motion of a scene's bounding box using rigid transformation along a
trajectory parameterized by a spline. We learn local deformations that conform
to the global trajectory using supervision from a text-to-video model. Our
approach enables the synthesis of scenes animated along arbitrary trajectories,
compositional scene generation, and significant improvements to the realism and
amount of generated motion, which we evaluate qualitatively and through a user
study. Video results can be viewed on our website:
https://sherwinbahmani.github.io/tc4d.