4K4DGen: Panoramic 4D Generation at 4K Resolution (2406.13527v3)

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 requirements of VR/AR applications that need free-viewpoint, 360$^{\circ}$ virtual views where users can move in all directions. 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$^{\circ}$ views at 4K (4096 $\times$ 2048) resolution, thereby providing an immersive user experience. Our method introduces a pipeline that facilitates natural scene animations and optimizes a set of dynamic 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 \textbf{Panoramic Denoiser} that adapts generic 2D diffusion priors to animate consistently in 360$^{\circ}$ images, transforming them into panoramic videos with dynamic scenes at targeted regions. Subsequently, we propose \textbf{Dynamic Panoramic Lifting} to 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 4K for the first time.

• The paper introduces a novel framework that generates high-resolution 4D panoramic videos from a single static image through distinct animating and lifting phases.
• It employs a Panoramic Denoiser to adapt 2D diffusion models for consistent panoramic video creation and a Spatial-Temporal Geometry Alignment for 4D scene optimization.
• Quantitative results, including higher CLIP scores and favorable user studies, demonstrate significant improvements in immersive VR/AR content generation.

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.