posecode

Why Posecode?

Animation clips and generated trajectories can show movement, but they often hide the semantic decisions that produced it.

Posecode keeps those decisions in readable source. A human can write the document, an animation tool can emit it, or an LLM can draft it. Parsing, validation, editing, and rendering do not require an AI model.

For example:

Bend your knees, move your hips backward, and keep your chest upright.

A human may understand that instruction, but a renderer cannot reliably determine:

• which joints should move,
• by how many degrees,
• in which coordinate frame,
• over what duration,
• in what sequence,
• or within which physical limits.

Humans, tools, and language models all need a shared syntax for expressing movement in a renderable and testable form.

Posecode provides that missing representation.

See Posecode in 28 seconds

From readable movement source to validated 3D rendering, MCP tools, and a one-script web embed.

Movement examples

Why Not Diffusion Text-to-Motion?

Neural text-to-motion systems can generate impressive movement, but they introduce problems for lightweight, programmable applications.

Resource intensive

Many systems require large models and GPU-backed inference, making real-time consumer deployment expensive.

Difficult to control

They usually produce coordinate trajectories rather than editable semantic instructions.

It is difficult to request a precise change such as:

Reduce knee flexion by 10 degrees during the second phase.

Hard to validate

Black-box trajectories do not naturally expose readable joint rules, phase definitions, or range-of-motion limits.

Hard to debug

When a movement looks wrong, developers may not know which semantic instruction caused the problem.

The Posecode Approach

Posecode uses a lightweight, text-driven pipeline.

• Readable: movements are stored as small .posecode documents.
• Structured: joints, actions, angles, timings, and constraints are explicit.
• Editable: developers and models can modify individual movement properties.
• Fast: parsing and rendering happen client-side.
• Deterministic: the same Posecode input produces the same validated representation.
• Inspectable: parser warnings and fidelity checks explain problems.
• Agent-friendly: the MCP server exposes generation, validation, critique, and sharing tools.
• Safety-aware: authored and IK-generated angles are constrained by configured range-of-motion limits.

The Idea in 30 Seconds

A .posecode file describes movement as timed phases with targeted joint actions.

OpenAI Build Week 2026: Posecode existed before the hackathon. During Build Week, the project was extended using Codex — running on GPT-5.6 — as the primary engineering tool for a real batch of shipped work: motion/grounding quality, language contract diagnostics, licensing restructuring, release automation, and product-facing pages. The sections below distinguish previous work from Build Week work using actual commit history, not a roadmap.

OpenAI Build Week Extension

What existed before Build Week

Before Build Week, Posecode already included:

• the core .posecode domain-specific language,
• a parser and intermediate motion representation,
• basic range-of-motion validation,
• a Three.js/WebGL renderer,
• forward kinematics,
• basic inverse-kinematics and ground-lock behavior,
• a browser playground,
• example movement files,
• shareable Posecode links,
• and an MCP server foundation.

This original version was developed primarily with Claude as an AI-assisted engineering tool.

That prior work provides the foundation for the project, but it is not presented as the new hackathon contribution.

What was added during Build Week

Every item below is a merged, dated pull request built with Codex (GPT-5.6) — see Build Week Evidence for direct links.

1. Motion and grounding overhaul — ROM-constrained reach IK, semantic palm/fist/sole/knee/pelvis contact surfaces, multi-contact refinement, stable support handoffs, and XBot-aware grounding (#76).
2. Language contract and diagnostics — Posecode language/IR v0.3 custom start-pose blocks with ROM-checked overrides, live and clip-wide grounding/self-collision diagnostics, and an accessible metric floor guide (#92).
3. Licensing restructure — split the monorepo into an Apache-2.0 standard layer (spec, parser, share, language, LSP, VS Code) and an AGPL-3.0 product layer (render, embed, MCP, eval, playground), with a commercial-license path (#84).
4. Release automation — Changesets-driven npm publishing via GitHub OIDC, MCP Registry publishing, and CI validation of package versions, entry points, and tarball contents (#66).
5. Third-party integration readiness — Posecode 0.2 timing vocabulary (drive/settle/flow/snap), a parser validation CLI, and embed compatibility metadata (#62).
6. Ground-lock correctness — parser-owned validation for per-side foot/hand/elbow ground locks and back-contact support for supine movements, replacing silent acceptance of invalid contacts (#61, #64).
7. LLM-first landing page and product page — redesigned the landing page around a prompt → Posecode → live 3D story, and added a /for-products page documenting the web component, parser, renderer, and MCP server for integrators (#82, #74).
8. Mobile and search fixes — mobile toolbar/viewer layout, natural hand orientation, and Google Search indexing corrections (#78, #65).

Build Week feature status

• Motion/grounding quality overhaul shipped (#76)
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