Hierarchical 3D Scene Creation with Isometric View: A New Approach

Generating 3D scenes at the scene level presents a significant challenge in the fields of multimedia and computer graphics. Existing approaches often struggle with limitations regarding object diversity or do not offer the necessary flexibility for interactive applications. A new research approach called HiScene promises a remedy. HiScene is a hierarchical framework that bridges the gap between 2D image generation and 3D object generation, producing detailed scenes with compositional identities and aesthetically pleasing content.

The core of HiScene lies in the consideration of scenes as hierarchical "objects" under an isometric perspective. A room functions as a complex object that can be broken down into individual, manipulable elements. This hierarchical approach allows for the generation of 3D content that is consistent with 2D representations while maintaining a compositional structure.

To ensure the completeness and spatial orientation of each decomposed element, HiScene uses a video diffusion-based amodal completion technique. This technique effectively handles occlusions and shadows between objects. Additionally, a shape prior injection is used to ensure spatial coherence within the scene.

The Advantages of the Hierarchical Approach

The hierarchical approach of HiScene offers several advantages over traditional methods. By decomposing the scene into individual objects, the editing and manipulation of individual elements is significantly simplified. This opens up new possibilities for interactive applications where users can dynamically change and customize the scene.

The isometric perspective allows for a clear and concise representation of the scene, making the spatial relationships between objects easier to understand. This is particularly important for applications in architecture, design, and game development.

Amodal Completion and Shape Prior Injection

The amodal completion technique plays a crucial role in generating complete and realistic objects. It allows for the reconstruction of occluded parts of objects, thus creating a complete picture of the scene, even if parts of the objects are hidden by other objects. The shape prior injection ensures that the generated objects are physically plausible and comply with the laws of perspective.

Experimental Results and Outlook

Initial experimental results show that HiScene generates more natural object placements and more complete object instances suitable for interactive applications compared to existing methods. At the same time, physical plausibility and consistency with user input are ensured.

HiScene has the potential to revolutionize 3D scene generation and open up new possibilities for interactive applications in various fields. Future research could focus on expanding object diversity and improving interaction possibilities.

HiScene and AI-Powered Content Creation

The development of HiScene underscores the growing importance of AI in content creation. By combining advanced algorithms and creative approaches, complex 3D scenes can be generated that were previously only possible with great effort. AI-powered tools like Mindverse offer the opportunity to make these technologies accessible to a wider audience and democratize content creation.

Bibliographie: Dong, W., Yang, B., Yang, Z., Li, Y., Hu, T., Bao, H., Ma, Y., & Cui, Z. (2025). HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation. arXiv preprint arXiv:2504.13072. Dong, W., Yang, B., Yang, Z., Li, Y., Hu, T., Bao, H., Ma, Y., & Cui, Z. (2025). HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation. arXiv preprint arXiv:2504.13072v1. Qiu, H., & Bao, H. (2024). BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation. arXiv preprint arXiv:2403.17298. Vincent, S. (2024). Incremental 3D scene graph construction for high level planning. Chang, A. X., Funkhouser, T., Savva, M., Halber, M., Nießner, M., & Mitra, N. J. (2023). Architect: Generating Vivid and Interactive 3D Indoor Scenes with Rooms and Furniture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 17643-17653).