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About this paper
Kunyu Feng, Yue Ma, Xinhua Zhang, Boshi Liu, Yikuang Yuluo, Yinhan Zhang, Runtao Liu, Hongyu Liu and 4 more
An MLLM-driven agent framework automates the synthesis of high-quality, diverse 2D, 3D, and 4D world data for AI-Generated Content. This framework, guided by models like GPT-4o, improves the performance of existing generative models for tasks such as object removal, 3D reconstruction, and 4D video generation, notably reducing FID from 62.48 to 60.32 and FVD from 160.72 to 155.71 in 4D generation.
Key insights
- State-of-the-art Multimodal Large Language Models (MLLMs) can serve as effective intelligent agents to automate and guide complex, multi-modal data synthesis processes for 2D, 3D, and 4D content.
- Multi-view optimization using VLMs is critical for achieving global semantic and physical consistency in generated 3D scenes, effectively reducing inconsistencies from single-view generation.
- VLM-guided frame prediction significantly enhances temporal coherence in 4D video synthesis, ensuring smooth motion and preventing abrupt inconsistencies between frames.
Summary by alphaXiv.
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