Sketch Yourself Your Own GTA 6 🖌️🗺️🌎
So essentially,
Sketch2Scene model converts drawing and words to 3D worlds!
Paper: Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches (12 Pages)
Github: https://xrvisionlabs.github.io/Sketch2Scene/
Researchers from XR Vision Labs are introducing Sketch2Scene, a new method for automatically creating interactive 3D game scenes from user sketches and text prompts.
Hmm..What’s the background?
Existing research on 3D content generation has primarily focused on small, single-object assets due to the lack of large, high-quality 3D scene datasets for model training. While recent approaches utilize 2D text-to-image models, depth estimation, or 3D Gaussian Splatting models, they often focus on indoor scenes or struggle with the complexity of large-scale outdoor environments. This research aims to overcome these limitations by introducing Sketch2Scene, a pipeline that combines user sketches and text prompts with pre-trained 2D diffusion models to generate interactive 3D game scenes.
Ok, So what is proposed in the research paper?
The researchers aim to address the challenge of generating large-scale, complex 3D scenes. Here's a breakdown of the key ideas:
Sketch-Guided Isometric Generation: The process begins by generating a 2D isometric image of the desired scene from the user's input sketch and text prompt
Visual Scene Understanding: The generated 2D isometric image is then analyzed to extract essential scene information with terrain, texture and foreground
Procedural 3D Scene Generation: Finally, the extracted scene information is used to generate the 3D scene within a game engine like Unity
By leveraging the strengths of 2D diffusion models, procedural generation, and innovative training techniques, Sketch2Scene presents a step forward in 3D content creation.
Source: Github
What’s next?
The researchers outline several promising avenues for future work to enhance the Sketch2Scene pipeline:
The current multi-stage pipeline, while effective, can lead to error accumulation. The authors suggest exploring concurrent generation of multiple modalities, such as RGB, semantic information, depth, surface material, and object footprints
Currently, terrain textures are limited by the available database used for retrieval. Developing diffusion-based models specifically for texture generation could dramatically expand the diversity and realism of terrain textures
While not explicitly mentioned, addressing error handling and improving user experience would be valuable for future iterations
So essentially,
Sketch2Scene model converts drawing and words to 3D worlds!
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