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How to reconstruct the garden shears example end to end with Agentic STAR

Run one supervised agent session on the committed example and get a GLB and pose.json.

  • Top pick
  • Research
  • Advanced
  • 5 hours
  • From the README
  • Claude Code

Who it is forA researcher or engineer who wants to see the full pipeline work on a known capture before using their own video.

The problemSetting up an agentic reconstruction harness is hard to judge without seeing a complete run on known data.

The outcomeA runs/ directory containing scene.py, mesh/object.glb, mesh/pose.json and per-pass renders under iterations/.

What you need first

  • Linux x86_64 host with an NVIDIA GPU
  • micromamba, git, curl, tar, xz, python3, tmux, bubblewrap, iproute2 and ffmpeg installed on the host
  • Root or CAP_NET_ADMIN for the sandbox network namespace
  • An Anthropic API key for Claude Code or an OpenAI API key for Codex
  • claude and/or codex available on PATH

Steps

  1. Run ./install.sh to build the artscript env, Blender 4.2.5 and the pi3x env.
  2. Run export ANTHROPIC_API_KEY=sk-ant-... in the shell you will use.
  3. Run tools/install_claude_bwrap.sh once so the key is stored in a state dir outside the checkout.
  4. Run tools/run_kf.sh --agent claude --timeout-hours 4 examples/garden_shears/capture:0.
  5. Run tools/claude-supervisor attach --run-dir runs/capture_kf_ to watch the run.

Prompt for your agent

In the agenticSTAR repo, set up the environment with ./install.sh and run the garden shears example with Claude Code using tools/run_kf.sh on examples/garden_shears/capture:0 on GPU 0. Expect runs/ to contain scene.py, mesh/object.glb and mesh/pose.json. Verify that mesh/pose.json holds per-frame poses and joint states before reporting success.

Caveat. A run takes hours and many tokens, so budget for model cost and use --timeout-hours to bound it.

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