  We present MAV3D (Make-A-Video3D), a method for generating three-dimensional
dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural
Radiance Field (NeRF), which is optimized for scene appearance, density, and
motion consistency by querying a Text-to-Video (T2V) diffusion-based model. The
dynamic video output generated from the provided text can be viewed from any
camera location and angle, and can be composited into any 3D environment. MAV3D
does not require any 3D or 4D data and the T2V model is trained only on
Text-Image pairs and unlabeled videos. We demonstrate the effectiveness of our
approach using comprehensive quantitative and qualitative experiments and show
an improvement over previously established internal baselines. To the best of
our knowledge, our method is the first to generate 3D dynamic scenes given a
text description.
