Compositional 3D-aware Video Generation with LLM Director
Authors: name: Hanxin Zhu, name: Tianyu He, name: Anni Tang, name: Junliang Guo, name: Zhibo Chen, name: Jiang Bian

Significant progress has been made in text-to-video generation through the
use of powerful generative models and large-scale internet data. However,
substantial challenges remain in precisely controlling individual concepts
within the generated video, such as the motion and appearance of specific
characters and the movement of viewpoints. In this work, we propose a novel
paradigm that generates each concept in 3D representation separately and then
composes them with priors from Large Language Models (LLM) and 2D diffusion
models. Specifically, given an input textual prompt, our scheme consists of
three stages: 1) We leverage LLM as the director to first decompose the complex
query into several sub-prompts that indicate individual concepts within the
video~(e.g., scene, objects, motions), then we let LLM to invoke
pre-trained expert models to obtain corresponding 3D representations of
concepts. 2) To compose these representations, we prompt multi-modal LLM to
produce coarse guidance on the scales and coordinates of trajectories for the
objects. 3) To make the generated frames adhere to natural image distribution,
we further leverage 2D diffusion priors and use Score Distillation Sampling to
refine the composition. Extensive experiments demonstrate that our method can
generate high-fidelity videos from text with diverse motion and flexible
control over each concept. Project page: https://aka.ms/c3v.