The project's README from github.com/deepopen-com/deepopen-3d-webs. As one markdown file: README.md.
中文 中文说明
Guangyu Zaojing (光域造景) is an image-driven 3D scene generation system. You supply one reference image and a prompt, and the system generates scene results along a camera trajectory that you can preview, replay, and trace. The official 3D demo assets play color/depth animations and Gaussian point clouds directly in the browser. The web app ships as a Docker image and listens on port 8090 by default.
A single image rarely turns into spatial content you can look around in, move through, and hand off. The traditional pipeline splits depth estimation, image completion, 3D representation, and camera flythrough across separate tools, so creators can't carry a scene from start to finish in one interface.
Guangyu Zaojing collapses that chain into a single studio:
The system runs in demo mode by default. Without a GPU, the pages, the gallery's 3D demos, and task replay all remain usable. With CUDA and model weights available, you can switch to the full 3D generation engine.
demo-* assets work well in classrooms and pitch sessionsThe system has five tabs.
Workbench
Summarizes total tasks, completed and failed counts, sample scene count, run mode, CUDA, disk, host, and service port. Click "Refresh System Data" to re-collect.
Scene Generation
Example Gallery
Lists examples by name, category, model, trajectory, and date. Previews show, together:
The status line indicates whether the 3D demo assets are mounted.
Task History
Generation actions are written to data/history.json. Official demo-* 3D demo tasks are preloaded. Enter an ID and click "View & Demo" to see task details, the reference image, both animations, the point cloud preview, and the PLY download together.
System Status
Shows host, OS, Python, CUDA, run mode, and the data directory, along with the recommended order of operations.
In demo mode, the preview loop runs on the input image; the official PLY / MP4 assets in the gallery and history can be demoed independently. When LD_DEMO_MODE=0 and the engine loads successfully, the full luciddreamer.py pipeline runs.
| Layer | Technology |
|---|---|
| UI | Gradio 3.50, multi-tab, Chinese |
| Web runtime | Python 3.9, FastAPI, Starlette, Uvicorn |
| Image / video | Pillow, NumPy, imageio |
| Scene generation | LucidDreamer: diffusion inpainting, depth estimation, 3D Gaussians |
| Depth | ZoeDepth |
| Gaussian rendering | 3D Gaussian Splatting rasterization and simple-knn |
| Point cloud preview | Down-sampling preview via webui/ply_preview.py |
| Task data | Local JSON (data/history.json) |
| Delivery | Docker Compose; demo image and CUDA image |
The web image uses requirements-web.txt. Full / CUDA dependencies are in requirements.txt and requirements-cuda.txt. Local machine and CUDA notes are in RUN_CUDA.md.
guang-yu-zao-jing
├── web_app.py Web entry point, listens on 8090
├── luciddreamer.py Main 3D generation pipeline
├── Dockerfile Demo image
├── Dockerfile.cuda Full CUDA image
├── docker-compose.yml Demo orchestration
├── docker-compose.cuda.yml CUDA orchestration
├── requirements-web.txt Web dependencies
├── requirements-cuda.txt CUDA dependencies
├── RUN_CUDA.md / start_*.bat Local and CUDA start guide
├── webui
│ ├── app.py Five tabs
│ ├── engine.py One-click / step-by-step generation and demo fallback
│ ├── catalog.py Example gallery
│ ├── history.py Task history and demo-* assets
│ ├── ply_preview.py PLY down-sampling and browser preview
│ ├── stats.py / styles.py Workbench stats and styling
│ └── paths.py Data and output paths
├── scene / gaussian_renderer Gaussian scene and rendering
├── utils / cameras / ZoeDepth
├── submodules Rasterization and simple-knn
├── examples Example images, prompts, official PLY / MP4
├── assets / data / outputs
├── scripts Deployment and local CUDA install/start scripts
└── docs Deployment docs, user manual, and screenshots
flowchart LR
Browser[Browser] --> WebApp[web_app.py]
WebApp --> UI[webui tabs]
UI --> Engine[engine.py]
Engine --> Demo[Demo preview]
Engine --> Core[luciddreamer.py]
Core --> Depth[ZoeDepth]
Core --> Gauss[Gaussian scene and rendering]
UI --> History[history and demo assets]
History --> Examples[examples PLY/MP4]
UI --> PlyPrev[ply_preview point cloud preview]
Engine --> Outputs[outputs and results]
All screenshots are from docs/user/images/.
Workbench
Scene Generation
Example Gallery · Reference image and 3D demo
Task History · View & Demo
System Status
Field descriptions and the order of operations for each tab are in the User Manual.
docker compose available8090 open on the local machine or serverLD_DEMO_MODE=1) needs no GPU — the default Dockerfile is enoughLD_DEMO_MODE=0 requires an NVIDIA GPU (16 GB VRAM recommended), the NVIDIA Container Toolkit or a local CUDA environment, requirements-cuda.txt / the CUDA build of PyTorch, and compiled submodules/simple-knn and submodules/depth-diff-gaussian-rasterization-minDemo (no GPU / default):
docker compose up -d --build
Linux server with GPU:
docker compose -f docker-compose.cuda.yml up -d --build
On Windows you can also double-click start_demo.bat / start_cuda.bat, or follow RUN_CUDA.md.
View logs and stop:
docker compose logs -f
docker compose down
Local:
http://127.0.0.1:8090
Server:
http://<server-ip>:8090
There is no login. Anyone who can open the address above in a browser can use the system. In production, restrict access by source with a firewall or reverse proxy.
Open the workbench and confirm the run mode and free disk space.
In the example gallery, pick an entry such as "Anime Lakeside Cabin" and click "Preview Example" to see the reference image, both animations, and the point cloud; download the full PLY if you need it.
Or, in task history, enter demo-animelake / demo-girl and click "View & Demo".
Go to Scene Generation, load an example or upload an image, and confirm the model, prompts, generation trajectory, and render trajectory.
Click "One-Click Generate". To bake the space only, use "Step 1 · Generate Gaussians"; to change the camera moves, use "Step 2 · Render Video".
Check the preview in the results area, then look up the details and media under the new task ID in Task History.
Image-driven 3D scene generation system. Users provide a reference image and prompt words, and the system generates previewable, replayable, and traceable scene results according to the camera trajectory; official 3D demonstration assets allow direct viewing of color/depth…