Text-To-4D Dynamic Scene Generation

We present MAV3D (Make-A-Video3D), a Generative models have seen tremendous recent progress, method for generating three-dimensional dynamic and can now generate realistic images from natural language scenes from text descriptions. Our approach uses prompts (Ramesh et al., 2022; Gafni et al., 2022; Rombach a 4D dynamic Neural Radiance Field (NeRF), et al., 2022; Saharia et al., 2022; Yu et al., 2022; Sheynin which is optimized for scene appearance, density, et al., 2022). This success has been extended beyond and motion consistency by querying a Text-to-2D images both temporally to synthesize videos (Singer Video (T2V) diffusion-based model. The dynamic et al., 2022; Ho et al., 2022) and spatially to produce 3D video output generated from the provided text can shapes (Poole et al., 2022; Lin et al., 2022; Nichol et al., be viewed from any camera location and angle, 2022b). However, these two categories of generative models and can be composited into any 3D environment.