Text-To-4D Dynamic Scene Generation
This enables text-to-4D generation for applications in virtual reality or content creation, representing a novel advancement rather than an incremental improvement.
The paper tackles the problem of generating 3D dynamic scenes from text descriptions, achieving this by using a 4D dynamic Neural Radiance Field optimized with a Text-to-Video diffusion model, resulting in viewable and composable scenes without requiring 3D or 4D data.
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.