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How to run a quick 6-view test on a consumer GPU with 4DAnyone

Produce a compact 360 degree six-view set with the Turbo default to check the pipeline works.

  • 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 with a consumer NVIDIA GPU who wants to confirm the install works before running heavier camera layouts.

The problemFull runs with many views take longer, so a first test needs a small, fast check.

The outcomeSix target videos in a 360 degree orbit, produced with the default distilled Turbo model.

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. git submodule update --init third_party/GVHMR
  4. conda create -n 4danyone python=3.11 -y then conda activate 4danyone
  5. pip install -r requirements.txt
  6. python scripts/download_example.py
  7. python inference.py --video_path "data/source/pexels/2785536-uhd_2160_3840_25fps.mp4" --output_dir "data/fdanyone/pexels/2785536-uhd_2160_3840_25fps" --views_per_layer 6 (inferred, check the README)

Prompt for your agent

In the 4DAnyone repo, run scripts/download_example.py and then inference.py with --views_per_layer 6 on the example video, writing to <output_dir>. Expected output: six target videos and cameras.json. Verify that videos/ holds 6 .mp4 files and that metadata.json records the run settings.

Caveat. The README says the example video path is the one used throughout, and the Turbo model is the default, so the Base model is not used unless you change the run settings.

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