qwen-mm-plugins-video-spatio

Computes spatial relationships in images and video using grounded object boxes and geometry tools.

2.9k|185|Updated Jul 29, 2026
One-click install
npx skills add https://github.com/QwenLM/Qwen-MM-Plugins --skill qwen-mm-plugins-video-spatio-qwenlm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qwen-mm-plugins-video-spatio
Source: https://github.com/QwenLM/Qwen-MM-Plugins/tree/main/src/capabilities/video-spatio/skill
Command: npx skills add https://github.com/QwenLM/Qwen-MM-Plugins --skill qwen-mm-plugins-video-spatio-qwenlm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Answering spatial questions about images and video—distance, size, orientation, relative position, camera motion, and cross-frame object counts—requires structured geometry rather than raw visual inspection. This Skill builds a coarse scene from grounded object boxes and depth estimates, then applies stateless geometry tools to reason about layout, viewpoints, and motion. ## Core Features & Use Cases - Scene Construction: Combine per-frame object boxes, depth estimates, and camera motions into a shared world-coordinate scene with bird's-eye-view visualization. - Spatial Analysis Tools: Triangulate distances, calibrate scale from known object sizes, track object and camera motion, count and match entities across frames, and reason about visibility from arbitrary viewpoints. - Exploration Planning: Assess coverage and reachability, then plan navigation or search steps for targets that are unobserved or grounded. - Use Case: Given a multi-room video walkthrough, ground furniture in sampled frames, build a scene, and determine whether a sofa is to the left or right of a table from a virtual viewpoint at the door facing the window. ## Quick Start Ask the agent to determine how far the chair is from the desk in this video and whether the camera moved between frames.

Frequently Asked Questions about qwen-mm-plugins-video-spatio

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I measure distance between objects in a video?

Ground the target object in two frames, supply camera motion between them, and call triangulate with the scene, target, and both frame indices. The tool requires a real camera baseline and reports whether the result is reliable; parallel or opposed rays cannot establish a position.

How to build a 3D scene from video frames for spatial reasoning?

Ground objects per frame as labeled boxes with depth estimates, then call build_scene with the frames and objects_by_frame. Add one camera_motions entry per adjacent frame pair for moving cameras; omitting motions assumes a static camera.

What bounding box format does the scene builder expect?

Boxes are [x1,y1,x2,y2] in 0-1000 normalized coordinates by default at any resolution. Set bbox_format to "pixels" or "normalized" explicitly for pixel or 0-1 coordinates, and never mix units within one scene.

Does this spatial reasoning require a GPU perception server?

No GPU perception server is required. The host model supplies boxes, depths, and camera-motion estimates, and geometry tools only compute from those inputs. VLM-backed tools like orient_facing use a configured OpenAI-compatible endpoint.

Why is my triangulation result unreliable?

Triangulation fails when camera rays are parallel, opposed, or backward, meaning there is no real baseline between the two frames. Recheck camera motion estimates, object correspondence across frames, and that the target is stationary before retrying.

Can depth estimates from images be treated as exact measurements?

No. Visual depth estimates are not calibrated measurements, so preserve uncertainty in answers. Use calibrate_scale with an object of known size to derive a correction factor for distance estimates when higher accuracy is needed.