让文本生成的4D场景实现真实复杂的物体变形与交互。
Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis
- 用多模态大模型生成物理感知的场景描述与过渡规划。
- 提出几何感知网络，实现复杂场景级4D过渡与形变。
- 适合游戏、影视等领域需真实动态场景生成的用户。
扩散模型在图像与视频生成方面取得显著进展，进一步提升了4D合成效果。现有4D生成方法虽能基于用户友好条件生成高质量4D物体或场景，但在复杂场景中实现显著物体形变与交互方面仍存在挑战。为此，我们提出Trans4D，一种新颖的文本到4D合成框架，支持真实复杂的场景过渡。首先，利用多模态大语言模型（MLLMs）生成物理感知的场景描述，用于4D场景初始化与有效过渡时机规划；随后，提出几何感知4D过渡网络，基于该规划实现场景级复杂过渡，包含丰富的几何形变。大量实验表明，Trans4D在生成具有准确且高质量过渡的4D场景方面，持续优于现有最先进方法，验证了其有效性。代码已开源：https://github.com/YangLing0818/Trans4D。
原文摘要 · Abstract (English)
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
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