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How to preview camera layouts in a browser GUI with 4DAnyone

Run the Gradio GUI to inspect inference results and visualize target views with Rerun.

  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Authorization notice. 4DAnyone is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA developer or artist who wants to see the generated views and camera layout before committing to a full pipeline run.

The problemReading cameras.json and folders of MP4 files by hand makes it hard to judge whether a camera layout suits the subject.

The outcomeA local web page at port 7860 showing the inference results for an existing output directory.

What you need first

  • A CUDA-capable NVIDIA GPU (README cites peak use below 24 GB and an RTX 4090 benchmark)
  • Git and conda installed
  • A source video that is a single person, 1080p or higher, 9:16 portrait, at least 121 frames
  • Permission to use the person shown in the source video

Steps

  1. git clone https://github.com/ant-research/4DAnyone.git
  2. cd 4DAnyone
  3. pip install -r requirements.txt
  4. pip install -r requirements-gui.txt
  5. python app.py --output_dir "data/fdanyone/pexels/2785536-uhd_2160_3840_25fps" --server_port 7860 (inferred, check the README)
  6. Open http://127.0.0.1:7860 in your browser.

Prompt for your agent

In the 4DAnyone repo, run app.py with --output_dir set to <output_dir> and --server_port 7860, then confirm the Gradio page loads at http://127.0.0.1:7860. Expected output: an interactive view of the inference results. Verify that the output directory contains cameras.json and videos/ before launching.

Caveat. The README says the GUI needs requirements-gui.txt installed in the same 4danyone environment, and a remote GPU server needs an SSH port forward to reach the page.

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