GenXD: Generating Any 3D and 4D Scenes (2411.02319v2)

Abstract: Recent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we propose to jointly investigate general 3D and 4D generation by leveraging camera and object movements commonly observed in daily life. Due to the lack of real-world 4D data in the community, we first propose a data curation pipeline to obtain camera poses and object motion strength from videos. Based on this pipeline, we introduce a large-scale real-world 4D scene dataset: CamVid-30K. By leveraging all the 3D and 4D data, we develop our framework, GenXD, which allows us to produce any 3D or 4D scene. We propose multiview-temporal modules, which disentangle camera and object movements, to seamlessly learn from both 3D and 4D data. Additionally, GenXD employs masked latent conditions to support a variety of conditioning views. GenXD can generate videos that follow the camera trajectory as well as consistent 3D views that can be lifted into 3D representations. We perform extensive evaluations across various real-world and synthetic datasets, demonstrating GenXD's effectiveness and versatility compared to previous methods in 3D and 4D generation.

• The paper introduces a unified framework combining latent diffusion and multiview-temporal modules to generate both 3D and 4D scenes.
• It employs a novel data curation pipeline to extract camera poses and object motions, creating the CamVid-30K dataset with 30K real-world samples.
• Extensive experiments show that GenXD outperforms state-of-the-art methods in single-view 3D object generation and few-view 3D scene reconstruction.

Overview of "X: Generating any 3D and 4D Scenes"

The paper "X: Generating any 3D and 4D Scenes" introduces a novel framework, X, designed to address the challenges associated with 3D and 4D scene generation. The focus is on leveraging both existing 3D and newly curated 4D datasets to train a unified model capable of producing high-quality scenes from minimal conditioning images. This approach aims to overcome limitations imposed by the lack of large-scale real-world 4D data and efficient model designs for dynamic scene representation.

The primary contribution of this work lies in its unified framework that handles both static (3D) and dynamic (4D) generation tasks seamlessly. The authors introduce a data curation pipeline that extracts both camera poses and object motion strengths from video inputs, culminating in the creation of a new dataset referred to as CamVid-30K. This dataset addresses a significant gap by incorporating approximately 30,000 real-world 4D data samples, thus providing a foundation for enhancing 4D generation models.

The X model capitalizes on a latent diffusion model to transform input conditions into 3D and 4D outputs. A particularly innovative aspect of the framework is its incorporation of multiview-temporal modules. These modules facilitate the disentanglement of spatial and temporal information, allowing the model to effectively learn from both 3D and 4D data. Furthermore, X employs masked latent conditions that support varying numbers of conditioning views, offering flexibility and scalability in generating consistent outputs across several applications.

The empirical assessments presented in the paper highlight the model's comparative advantage over existing methods. Evaluations conducted across a spectrum of tasks demonstrate that the X framework not only competes with but often surpasses state-of-the-art methods in both 3D object and scene generation, as well as in 4D video generation. Notably, X achieves significant performance improvements in both single-view 3D object generation and few-view 3D scene reconstruction.

Implications and Future Developments

The strong performance of X implies wide-ranging implications for industries reliant on 3D content generation, such as gaming, augmented reality, and virtual reality. By providing a robust mechanism for generating high-quality 3D and 4D scenes, the framework can enhance content creation workflows, reduce resource dependencies, and enable more immersive user experiences.

Theoretically, the insight provided by the multiview-temporal modules contributes to a deeper understanding of how spatial and temporal information can be decoupled and subsequently leveraged for scene generation. This could inform future architectures targeting similar generative tasks beyond the scope of this paper.

Looking forward, the introduction of the CamVid-30K dataset opens avenues for further research into more realistic and dynamic scene generation. Future developments may focus on expanding the diversity and complexity of such datasets, enabling models like X to generalize better to real-world scenarios. Additionally, refinements to the model's architecture may enhance its ability to capture finer details in highly dynamic environments, potentially integrating concepts from physics-based simulations or neural rendering techniques.

In summary, this paper represents a substantial step toward more versatile and scalable generative models, emphasizing the importance of integrating diverse datasets and novel architectural components to push the boundaries in computer-generated scene realism.