SceneDreamer

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SceneDreamer is an image generation tool that creates unbounded 3D scenes from 2D image collections. It synthesizes diverse landscapes with 3D consistency and free camera trajectory.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is SceneDreamer?

SceneDreamer is an advanced AI tool designed for generating unbounded 3D scenes from existing 2D image collections. It leverages deep learning to synthesize diverse landscapes across various styles, ensuring 3D consistency, well-defined depth, and allowing for free camera trajectory. The tool is particularly adept at creating immersive 3D environments from 'in-the-wild' 2D images. It offers both inference capabilities for generating scenes and comprehensive training code for users who wish to customize or expand its functionalities. A Gradio demo is available for local use, alongside options for rendering high-resolution outputs and modifying various rendering parameters like camera mode, resolution, and scene size.

Best used for

Ideal for graphic designers and UX designers who need to generate realistic and diverse 3D scenes, create immersive virtual environments, and prototype architectural visualizations. Especially valuable for those requiring 3D consistency and free camera trajectory from 2D image collections.

Common actions

generate 3D scenes

create virtual environments

synthesize landscapes

render 3D models

workflowsopen-source"AI Agents"deepfakecollaborationface swappinglow-code/no-codegithub copilotautomated workflow

Capabilities

Key features

Unbounded 3D scene generation
2D image collection input
3D consistency
Free camera trajectory
Diverse landscape synthesis
Customizable rendering parameters
Gradio demo available

Target Audience

graphic designerux designer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of 2D image collections can be used to generate 3D scenes?

SceneDreamer is designed to work with 'in-the-wild' 2D image collections. The project mentions using 1.1 million images collected from the web for training, and suggests public datasets like LHQ for paired images and segmentation maps. Users can prepare their own datasets following the specified structure.

What are the hardware requirements for running SceneDreamer?

While specific hardware requirements are not explicitly detailed, the installation instructions involve compiling third-party libraries with CUDA, indicating the need for an NVIDIA GPU. Training also defaults to using 8 GPUs, suggesting significant computational resources are beneficial for advanced use.

Can I customize the camera trajectory and rendering output?

Yes, SceneDreamer allows for extensive customization of rendering parameters. You can modify the camera mode, total number of frames (cam_maxstep), resolution, number of sampled points per camera ray, camera FOV (cam_ang), and the spatial resolution of the sampled scene (scene_size) via the configuration file.

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