4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency

arXiv:2312.17225v2 Announce Type: replace
Abstract: Aided by text-to-image and text-to-video diffusion models, existing 4D content creation pipelines utilize score distillation sampling to optimize the entire dynamic 3D scene. However, as these pipelines generate 4D content from text or image inputs, they incur significant time and effort in prompt engineering through trial and error. This work introduces 4DGen, a novel, holistic framework for grounded 4D content creation that decomposes the 4D generation task into multiple stages. We identify static 3D assets …

arxiv content generation cs.cv spatial temporal type