3D Scene Generation
Free 3D scene generation AI tools for creating immersive environments for games, films, and virtual experiences with ease.
DreamBeast can generate unique 3D animal assets with different parts. It uses a method from Stable Diffusion 3 to quickly create detailed Part-Affinity maps from various camera views, improving quality while saving computing power.
And talking about Splats, Feature Splatting can manipulate both the appearance and the physical properties of objects in a 3D scene using text prompts.
CityGaussian can render large-scale 3D scenes in real-time using a divide-and-conquer training approach and Level-of-Detail strategy. It achieves high-quality rendering at an average speed of 36 FPS on an A100 GPU.
3DWire can generate 3D house wireframes from text! The wireframes can be easily segmented into distinct components, such as walls, roofs, and rooms, reflecting the semantic essence of the shape.
WildGaussians is a new 3D Gaussian Splatting method that can handle occlusions and appearance changes. The method is able to achieve real-time rendering speeds and is able to handle in-the-wild data better than other methods.
LiveScene can identify and control multiple objects in complex scenes. It is able to locate individual objects in different states and enables control of them using natural language.
Toon3D can generate 3D scenes from two or more cartoon drawings. It’s far from perfect, but still pretty cool!
DreamScene4D can generate dynamic 4D scenes from single videos. It tracks object motion and handles complex movements, allowing for accurate 2D point tracking by converting 3D paths to 2D.
Invisible Stitch can inpaint missing depth information in a 3D scene, resulting in improved geometric coherence and smoother transitions between frames.
Video2Game can turn real-world videos into interactive game environments. It uses a neural radiance fields (NeRF) module for capturing scenes, a mesh module for faster rendering, and a physics module for realistic object interactions.
LoopGaussian can convert multi-view images of a stationary scene into authentic 3D cinemagraphs. The 3D cinemagraphs can be rendered from a novel viewpoint to obtain a natural seamless loopable video.
GEM3D is a deep, topology-aware generative model of 3D shapes. The method is able to generate diverse and plausible 3D shapes from user-modeled skeletons, making it possible to draw the rough structure of an object and have the model fill in the rest.
Argus3D can generate 3D meshes from images and text prompts as well as unique textures for its generated shapes. Just imagine composing a 3D scene and fill it with objects by pointing at a space and using natural language to describe what you want to place there.
Spacetime Gaussian Feature Splatting is a novel dynamic scene representation that is able to capture static, dynamic, as well as transient content within a scene and can render them at 8K resolution and 60 FPS on an RTX 4090.
PhysGaussian is a simulation-rendering pipeline that can simulate the physics of 3D Gaussian Splats while simultaneously render photorealistic results. The method supports flexible dynamics, a diverse range of materials as well as collisions.
DreamCraft3D can create high-quality 3D objects from a single prompt. It uses a 2D reference image to guide the sculpting of the 3D object and then improves texture fidelity by running it through a fine-tuned Dreambooth model.
3D Gaussian Splatting can create high-quality 3D scenes in real-time at 1080p resolution with over 30 frames per second. It uses 3D Gaussians for efficient scene representation and a fast rendering method, achieving competitive training times while maintaining great visual quality.
NIS-SLAM can reconstruct high-fidelity surfaces and geometry from RGB-D frames. It also learns 3D consistent semantic representations during this process.
It’s said that our eyes hold the universe. When it comes to the method discussed in the paper Seeing the World through Your Eyes, they at least hold a 3D scene. The method discussed in the paper is able to reconstruct 3D scenes beyond the camera’s line-of-sight using portrait images containing eye reflections.
Neuralangelo can reconstruct detailed 3D surfaces from RGB video captures. It uses multi-resolution 3D hash grids and neural surface rendering, achieving high fidelity without needing extra depth inputs.