Festival 4D

Turn multiple fan-shot smartphone videos of the same concert into a synchronized, explorable 4D experience: time-aligned multi-video playback, 3D scene reconstruction with camera poses, a free-roam "god's eye" viewer, AR-style overlays projected onto each video, and AI-tagged moments on a shared timeline.

Status: foundation phase. The scaffold, database, frozen contracts (API, pose math, DB schema), and a synthetic fixture generator are in place. Feature lanes (media sync, reconstruction, viewer, AI events) build on top. The full README with the real-footage workflow is written in the integration phase (M9). See OPUS_BUILD_INSTRUCTIONS.md for the canonical spec and plan/ for the execution plan.

Prerequisites

• Python 3.11+ and uv (or venv + pip)
• ffmpeg / ffprobe on your PATH (required)
• COLMAP (optional — reconstruction degrades gracefully without it)
• A classifier API key (optional — GEMINI_API_KEY by default; see events_ai.py)
• Node 18+ for the frontend

Quickstart (synthetic demo — no footage needed)

# 1. backend env
uv venv --python 3.12
uv pip install -e ".[dev]"

# 2. generate the synthetic fixture project (fake videos + poses + point cloud + events)
uv run python -m festival4d synthetic

# 3. serve the API (http://127.0.0.1:8000)
uv run python -m festival4d serve

# 4. in another terminal, the frontend
cd frontend
npm install
npm run dev            # http://localhost:5173

Backend CLI

python -m festival4d synthetic     # generate the synthetic fixture (M0)
python -m festival4d ingest        # probe videos + extract audio      (lane A / M1)
python -m festival4d sync          # GCC-PHAT audio alignment           (lane A / M1)
python -m festival4d reconstruct   # COLMAP SfM + pose export           (lane B / M2)
python -m festival4d events        # audio candidates + AI classify     (lane D / M7)
python -m festival4d serve         # FastAPI app                        (M3)

Tests

uv run pytest