Trans4D: Realistic Geometry-Aware Transition for Compositional
  Text-to-4D Synthesis
Authors: name: Bohan Zeng, name: Ling Yang, name: Siyu Li, name: Jiaming Liu, name: Zixiang Zhang, name: Juanxi Tian, name: Kaixin Zhu, name: Yongzhen Guo, name: Fu-Yun Wang, name: Minkai Xu, name: Stefano Ermon, name: Wentao Zhang

Recent advances in diffusion models have demonstrated exceptional
capabilities in image and video generation, further improving the effectiveness
of 4D synthesis. Existing 4D generation methods can generate high-quality 4D
objects or scenes based on user-friendly conditions, benefiting the gaming and
video industries. However, these methods struggle to synthesize significant
object deformation of complex 4D transitions and interactions within scenes. To
address this challenge, we propose Trans4D, a novel text-to-4D synthesis
framework that enables realistic complex scene transitions. Specifically, we
first use multi-modal large language models (MLLMs) to produce a physic-aware
scene description for 4D scene initialization and effective transition timing
planning. Then we propose a geometry-aware 4D transition network to realize a
complex scene-level 4D transition based on the plan, which involves expressive
geometrical object deformation. Extensive experiments demonstrate that Trans4D
consistently outperforms existing state-of-the-art methods in generating 4D
scenes with accurate and high-quality transitions, validating its
effectiveness. Code: https://github.com/YangLing0818/Trans4D