LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field (2404.08966v2)

Abstract: Cinemagraph is a unique form of visual media that combines elements of still photography and subtle motion to create a captivating experience. However, the majority of videos generated by recent works lack depth information and are confined to the constraints of 2D image space. In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling. To achieve this, we first employ the 3D-GS method to reconstruct 3D Gaussian point clouds from multi-view images of static scenes,incorporating shape regularization terms to prevent blurring or artifacts caused by object deformation. We then adopt an autoencoder tailored for 3D Gaussian to project it into feature space. To maintain the local continuity of the scene, we devise SuperGaussian for clustering based on the acquired features. By calculating the similarity between clusters and employing a two-stage estimation method, we derive an Eulerian motion field to describe velocities across the entire scene. The 3D Gaussian points then move within the estimated Eulerian motion field. Through bidirectional animation techniques, we ultimately generate a 3D Cinemagraph that exhibits natural and seamlessly loopable dynamics. Experiment results validate the effectiveness of our approach, demonstrating high-quality and visually appealing scene generation. The project is available at https://pokerlishao.github.io/LoopGaussian/.

• The paper introduces a novel 3D cinemagraph generation framework using 3D Gaussian modeling and Eulerian motion fields to produce loopable, natural animations.
• It details a multi-phase process including point cloud reconstruction, feature projection, and clustering to mitigate object deformation and enhance depth representation.
• The approach leverages scene self-similarity for efficient velocity estimation, reducing computational demands while enabling rendering from novel viewpoints.

LoopGaussian: Elevating Cinemagraphs into 3D Space Through Eulerian Motion Field

Introduction to 3D Cinemagraph Generation

Cinemagraphs, a fusion between still photography and video, have long captivated viewers by infusing static scenes with subtle, repeating movements. While existing methodologies primarily generate cinemagraphs within 2D confines, the advent of 3D cinemagraphs promises an immersive experience by incorporating depth, a critical component for augmented and mixed reality applications. The work presented by Jiyang Li et al. introduces a pioneering approach, LoopGaussian, which not only transitions cinemagraph creation to 3D space but also mitigates the limitations of current techniques that lack three-dimensional geometric structure or necessitate constricted camera movement.

Core Methodology

The LoopGaussian framework is based on a novel application of 3D Gaussian modeling for crafting 3D cinemagraphs from multi-view images. Central to this approach is the 3D Gaussian Splatting (3D-GS) method for reconstructing 3D scenes, enhanced by shape regularization to prevent artifacts due to object deformation. The proposed method involves several key phases:

1. Initial reconstruction of 3D Gaussian point clouds from static scenes,
2. Feature space projection and clustering of 3D Gaussians using an autoencoder and a custom clustering technique called SuperGaussian,
3. Construction of an Eulerian motion field by exploiting scene self-similarity and a two-stage optimization for velocity estimation.

The generated 3D cinemagraphs manifest loopable, natural dynamics that can be rendered from novel viewpoints, thereby amplifying the visual appeal and realism of the scenes.

Technical Contributions and Implications

The LoopGaussian framework introduces several significant innovations in the domain of dynamic scene generation and novel view synthesis:

• 3D Cinemagraph Generation: It pioneers the generation of authentic 3D cinemagraphs, enabling natural and loopable scene dynamics while accommodating rendering from various viewpoints, a stark advancement compared to 2D-centric previous works.
• Eulerian Motion Field in 3D: By adopting an Eulerian approach to describe scene dynamics, the paper ventures into new territory, significantly diverging from traditional Lagrangian methods and offering a flexible means to depict complex motion patterns.
• Self-Similarity-Based Optimization: The framework smartly leverages scene self-similarity to estimate motion fields without relying on extensive pre-training on large datasets, presenting a heuristic approach that simultaneously reduces computational demands and data dependence.

Future Outlook

Looking ahead, LoopGaussian's ability to generate captivating 3D cinemagraphs with seamless loopable dynamics opens a plethora of research avenues and practical applications, particularly in enhancing virtual and augmented reality experiences. Further exploration into optimizing the efficiency of 3D Gaussian clustering and motion field estimation could pave the way for real-time applications. Additionally, expanding the framework to incorporate interactive elements or adapt dynamically to user inputs could significantly enrich user experiences in digital storytelling, gaming, and mixed reality environments.

Conclusion

The introduction of LoopGaussian by Jiyang Li and colleagues marks a significant advancement in the field of cinemagraphs and 3D scene reconstruction. By enabling the generation of authentic 3D cinemagraphs from multi-view images and employing Eulerian motion fields to depict scene dynamics, this work not only broadens the horizon for digital visual creation but also sets a new benchmark for future research in the field of dynamic 3D scene generation.