Abstract
The blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on dynamic objects or perform outpainting from a single perspective image, failing to meet the needs of VR/AR applications. In this work, we tackle the challenging task of elevating a single panorama to an immersive 4D experience. For the first time, we demonstrate the capability to generate omnidirectional dynamic scenes with 360-degree views at 4K resolution, thereby providing an immersive user experience. Our method introduces a pipeline that facilitates natural scene animations and optimizes a set of 4D Gaussians using efficient splatting techniques for real-time exploration. To overcome the lack of scene-scale annotated 4D data and models, especially in panoramic formats, we propose a novel Panoramic Denoiser that adapts generic 2D diffusion priors to animate consistently in 360-degree images, transforming them into panoramic videos with dynamic scenes at targeted regions. Subsequently, we elevate the panoramic video into a 4D immersive environment while preserving spatial and temporal consistency. By transferring prior knowledge from 2D models in the perspective domain to the panoramic domain and the 4D lifting with spatial appearance and geometry regularization, we achieve high-quality Panorama-to-4D generation at a resolution of (4096 $\times$ 2048) for the first time. See the project website at https://4k4dgen.github.io.
Overview
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The 4K4DGen framework is designed to create high-quality, immersive 4D panoramic environments, addressing the challenges of generating such content by using a novel Panoramic Denoiser and structured sets of 3D Gaussians.
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The framework operates in two key phases: the animating phase, which generates panoramic videos from static images using a Panoramic Denoiser, and the lifting phase, which elevates these videos into 4D environments through Spatial-Temporal Geometry Alignment and depth maps.
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4K4DGen significantly outperforms existing techniques in terms of visual quality and consistency, making substantial contributions to applications in VR/AR, movie production, and interactive media.
4K4DGen: Panoramic 4D Generation at 4K Resolution
The paper "4K4DGen: Panoramic 4D Generation at 4K Resolution" introduces a novel framework designed to create high-quality, immersive 4D panoramic environments. The increasing demand for virtual reality and augmented reality (VR/AR) technologies necessitates the development of high-resolution, dynamic environments that support seamless, 360-degree panoramic views and 6-DoF virtual tours. Despite significant advances in 2D image, video, and 3D generation, the generation of panoramic 4D content has remained underdeveloped due to the scarcity of high-quality training data and specialized models.
Overview of 4K4DGen
The 4K4DGen framework addresses these challenges by facilitating the generation of 4K resolution omnidirectional dynamic scenes from a single static panoramic image. The proposed method operates in two key phases: the animating phase and the 4D lifting phase.
Animating Phase
The animating phase is centered around the generation of panoramic videos from static panoramic images. This is achieved through a novel Panoramic Denoiser that adapts pre-trained 2D perspective image-to-video (I2V) diffusion models to the spherical latent codes in panoramic formats. Traditional I2V models trained on perspective images tend to produce minor motions or inconsistencies when applied to panoramic images due to domain differences and resolution constraints. The Panoramic Denoiser overcomes these issues by projecting the spherical latent code into multiple perspective views, simultaneously denoising them, and fusing the results to ensure global coherence and cross-view consistency.
Lifting Phase
In the lifting phase, the generated panoramic video is elevated into a 4D immersive environment. This involves the optimization of scene geometry through Spatial-Temporal Geometry Alignment, ensuring spatial and temporal consistency. A depth estimator enriches the process by generating consistent panoramic depth maps. These maps are fused to create a coherent 4D scene representation using structured sets of 3D Gaussians. The rendering of the 4D scene is facilitated by efficient splatting techniques, allowing for real-time exploration of dynamic scenes with high spatial and temporal fidelity.
Numerical Results and Claims
The paper presents strong numerical results, quantifying the improvements in both visual quality and consistency of the generated scenes. Evaluative metrics such as CLIP consistency and user studies demonstrate that the proposed method significantly outperforms existing techniques like 3D-Cinemagraphy. Specifically, 4K4DGen achieves higher CLIP similarity scores and is preferred by users in terms of visual quality and cross-view consistency.
Implications and Future Developments
The practical implications of 4K4DGen are substantial for the fields of VR/AR, movie production, and interactive media. By enabling the generation of high-resolution, dynamic 4D panoramic environments, this work paves the way for more immersive and interactive virtual experiences. Theoretically, the adaptation of 2D diffusion models to panoramic formats and the successful lifting of 2D dynamics into 4D environments represent significant advancements in generative modeling.
However, the paper also acknowledges certain limitations, such as the dependence on the quality of pre-trained I2V models for temporal animation and the substantial storage requirements for high-resolution 4D representations. Future research could focus on integrating more advanced 2D animators and exploring techniques for model distillation and pruning to optimize storage.
Conclusion
In conclusion, the 4K4DGen framework represents a significant step forward in the generation of high-quality, immersive VR/AR content. By addressing the challenges unique to panoramic 4D content generation through innovative denoising and lifting techniques, 4K4DGen enables real-time exploration of dynamic, high-resolution scenes. The proposed method not only enhances user experience but also opens new avenues for future research in AI-driven content creation for immersive technologies.
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