Computer Animation - Exercise

Overview

This repository contains the code for the computer animation exercise. The code uses Newton@2a6df66 as a backend for the physics simulation.

Related MCP server: chuk-mcp-stage

Agent scene framework

The repository also contains a prompt-driven framework for generating animations:

• ca_framework.scene defines a backend-neutral, JSON-serializable scene model for rigid bodies, cloth, fluids, constraints, and external fields.

• ca_framework.mcp provides protocol-independent scene editing tools.

• mcp_server exposes those tools through the official Python MCP SDK as a standalone stdio or Streamable HTTP server.

• knowledge/skills contains focused simulation knowledge that an agent can load when translating a prompt into a scene.

The runtime and agent-facing files intentionally contain no reference scenes or reference answers. Black-box task prompts and benchmark commands live under evaluation/; do not expose that directory to an agent during a prompt-to-video experiment.

Start the stdio MCP server from the repository root:

uv run --project mcp_server ca-scene-mcp

Scene files are stored in .ca-scenes by default. Set CA_SCENE_WORKSPACE to use another directory. SceneExecutorLocal compiles rigid bodies and cloth to Newton XPBD/VBD, advances smoke and APIC/FLIP fluid routes, caches every simulated frame, and renders an output bundle. Final jobs use run_scene(scene_name, output_dir) and write animation.mp4, scene.json, program.py, metrics.json, diagnostics.jsonl, and cache/.

Development tests remain under tests/. Evaluation assets and instructions are documented separately in evaluation/README.md.

Numerical optimization

Scene optimization is a separate, reproducible workflow: an OptimizationPlan defines bounded physical parameters, an optional TaskSpec defines task losses, and Optuna proposes Random, TPE, or CMA-ES trials. Every trial passes through static validation, a short coarse rejection run, a full-resolution metric run, and optional finalist rendering. Optimization history is never stored in Scene.metadata.

The MCP server exposes the complete lifecycle through create_optimization_plan, validate_optimization_plan, start_optimization, get_optimization_job, list_optimization_trials, get_optimization_trial, compare_optimization_trials, and apply_optimization_trial. A stored plan has three independent inputs:

.ca-scenes/.optimizations/plans/<name>/
├── scene.json
├── optimization_plan.json
└── task_spec.json          # optional

Trial directories contain the applied scene and parameters, constraint-first metrics, diagnostics, structured telemetry, and the final report. The report selects the best feasible, fastest acceptable, and most robust candidates and writes best_so_far.svg without requiring a plotting dependency.

Installation

1. Install Git and uv on your system. On Windows, uv can be simply installed by running the following command in PowerShell:

   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Clone this repository:

   git clone http://dalab.se.sjtu.edu.cn/gitlab/courses/ca-framework-2026.git

3. Install dependencies:

   uv sync --extra examples

4. Use any IDE (PyCharm is recommended) of your choice to open the project and run the script in examples/ca_exercises/ directory. PyCharm will automatically recognize the virtual environments, if not, you can activate in the terminal using the command below:

   ./.venv/Scripts/activate

5. Then run the python script in the terminal:

   python ./newton/examples/ca_exercises/exercise2_xxx_xxx.py

   If everything's alright, you will see a window like below: Controls:

   • WASD: move camera

   • QE: move camera up/down

   • Left click: lock around

Handin

You only need to modify the TODO sections in the code. You can run the code to test your implementation, but please do not modify the code structure or add any new files.

After you finish one exercise, You only need to submit the modified .py files in newton/_src/solvers/ca_exercises/. Please compress these files into a zip file and submit it to Canvas. The file name should be in the format of ca_exercise<n>_<student_id>.zip.

Notes: each exercise may have its own additional requirements, please pay attention.