TiP4GEN: Text to Immersive Panorama 4D Scene Generation
Ke Xing, Hanwen Liang, Dejia Xu, Yuyang Yin, Konstantinos N. Plataniotis, Yao Zhao, Yunchao Wei
Abstract
With the rapid advancement and widespread adoption of VR/AR technologies, there is a growing demand for the creation of high-quality, immersive dynamic scenes. However, existing generation works predominantly concentrate on the creation of static scenes or narrow perspective-view dynamic scenes, falling short of delivering a truly 360-degree immersive experience from any viewpoint. In this paper, we introduce TiP4GEN, an advanced text-to-dynamic panorama scene generation framework that enables fine-grained content control and synthesizes motion-rich, geometry-consistent panoramic 4D scenes. TiP4GEN integrates panorama video generation and dynamic scene reconstruction to create 360-degree immersive virtual environments. For video generation, we introduce a Dual-branch Generation Model consisting of a panorama branch and a perspective branch, responsible for global and local view generation, respectively. A bidirectional cross-attention mechanism facilitates comprehensive information exchange between the branches. For scene reconstruction, we propose a Geometry-aligned Reconstruction Model based on 3D Gaussian Splatting. By aligning spatial-temporal point clouds using metric depth maps and initializing scene cameras with estimated poses, our method ensures geometric consistency and temporal coherence for the reconstructed scenes. Extensive experiments demonstrate the effectiveness of our proposed designs and the superiority of TiP4GEN in generating visually compelling and motion-coherent dynamic panoramic scenes.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 9d351ffb-04a8-44da-9ec0-297ead5ec97bCited by top-tier papers2
- PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video DiffusionYuyang Yin, Hao-Xiang Guo, Fangfu Liu, Mengyu Wang et al.ICML 2026 · 3 citations
- OmniRoam: World Wandering via Long-Horizon Panoramic Video GenerationYuheng Liu, Xin Lin, Xinke Li, Baihan Yang et al.SIGGRAPH 2026 · 1 citation
Builds on43
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Directly Denoising Diffusion ModelsDan Zhang, Jingjing Wang, Feng LuoICML 2024 · 11,724 citations
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li et al.NeurIPS 2022 · 8,965 citations
Related papers
- 4K4DGen: Panoramic 4D Generation at 4K ResolutionRenjie Li, Panwang Pan, Bangbang Yang, Dejia Xu et al.ICLR 2025
- 360Explorer: Exploring 4D Controllable World in Panoramic VideosXinhua Cheng, Haiyang Zhou, Wangbo Yu, Tanghui Jia et al.AAAI 2026
- HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene GenerationHaiyang Zhou, Wangbo Yu, Jiawen Guan, Xinhua Cheng et al.ACM MM 2025 · 4 citations
- 4DSurf: High-Fidelity Dynamic Scene Surface ReconstructionRenjie Wu, Hongdong Li, José M. Álvarez, Miaomiao LiuCVPR 2026
- Dynamic Gaussian Scene Reconstruction from Unsynchronized VideosZhixin Xu, Hengyu Zhou, Yuan Liu, Wenhan Xue et al.AAAI 2026
