Get Started
Back to Templates

Automate 3D Body Model Generation from Images Using SAM-3D & Google Sheets

Created by

Created by: Davide || n3witalia

Davide

Last update

Last update 21 hours ago

Categories

Share

This workflow automates the process of generating 3D human body models (in .glb format) from single image using SAM-3D model. It operates by connecting a Google Sheet as a data source with the external AI processing API.

Start Result
image image

Use Cases

1. ✅ Sports Analysis & Motion Optimization

3D models allow precise analysis of posture, angles, and technique.
Possible applications:

  • Golf swing analysis
    Identify stance, rotation, shoulder/hip alignment, and follow-through.
  • Tennis serve biomechanics
    Optimize shoulder rotation, racket angle, leg push-off.
  • Running gait analysis
    Evaluate stride symmetry, foot strike, and body tilt.
  • Cycling posture optimization
    Reduce drag by analyzing torso angle and hand position.
  • Swimming technique evaluations
    Compare ideal vs. actual joint angles.

2. ✅ Fitness, Health & Physiotherapy

3D models can visually highlight imbalances or incorrect positions.

  • Posture correction assessments
    Identify spinal misalignment or uneven weight distribution.
  • Physical therapy progress tracking
    Compare poses over time to assess recovery.
  • Ergonomics and workplace safety
    Evaluate whether a worker’s posture is safe during lifting or repetitive tasks.
  • Home fitness coaching
    Automated feedback for yoga, pilates, stretching exercises.

3. ✅ Fashion, Apparel & Virtual Try-On

Photorealistic body reconstruction helps generate tailored outfits or evaluate fit.

  • Virtual try-on for clothing brands
    Produce accurate 3D avatars to test garments digitally.
  • Custom-made fashion
    Use 3D measurements for bespoke tailoring patterns.
  • Model pose simulation
    Test clothing fit in dynamic or unusual positions (e.g., dance, athletic poses).

4. ✅ Gaming, Animation & Digital Content Creation

Quick 3D reconstruction reduces production time for digital humans.

  • Character rigging from real people
    Generate 3D avatars ready for animation.
  • Motion capture alternatives
    Recreate specific poses without expensive mocap systems.
  • VR/AR content creation
    Deploy 3D characters into immersive environments.
  • Comics, illustration, and concept art
    Use 3D poses as reference models to speed up drawing.

5. ✅ Medical, Research & Educational Applications

Human-body 3D models provide insights in scientific or practical contexts.

  • Anthropometric measurements
    Estimate height, limb length, body proportions from images.
  • Posture and musculoskeletal studies
    Analyze joint angle distribution in different poses.
  • Rehabilitation robotics or exoskeleton design
    Fit devices to a patient’s real body shape.
  • Training materials for anatomy or movement science
    Generate accurate pose examples for students.

6.✅ Security, Forensics & Reconstruction

When allowed ethically and legally, 3D models can support investigations.

  • Reconstruction of accident scenes
    Understand how a person fell, collided, or moved.
  • Analysis of body posture in video frames
    Useful for determining gesture patterns or mobility constraints.

7. ✅ Art, Photography & Creative Industries

Artists often need unusual or complex human poses.

  • Pose reference creation
    For painting, 3D sculpting, illustration, or storyboarding.
  • Recreating dynamic action scenes
    Parkour, martial arts, ballet, expressive dance.
  • Virtual studio lighting tests
    Apply simulated lighting to a 3D model before shooting.

How It Works

This workflow automates the process of generating 3D human body models (in .glb format) from single images using the FAL.AI SAM-3D service. It operates by connecting a Google Sheet as a data source with the external AI processing API. Here is the operational flow:

  1. Trigger & Data Fetch: The workflow begins either manually (via "Test workflow") or on a schedule. It queries a designated Google Sheet to find rows where the "3D RESULT" column is empty, indicating a new image needs processing.
  2. API Request & Queuing: For each new image, it sends the image URL to the FAL.AI SAM-3D API endpoint (/fal-ai/sam-3/3d-body), which queues the job and returns a unique request_id.
  3. Status Polling & Waiting: The workflow enters a polling loop. It waits 60 seconds, then checks the job's status using the request_id. If the status is not "COMPLETED", it waits another 60 seconds and checks again.
  4. Result Retrieval & Storage: Once the status is "COMPLETED", the workflow fetches the final result, which contains the URL of the generated 3D model file (.glb). This file is then downloaded via an HTTP request.
  5. Sheet Update: Finally, the workflow updates the original Google Sheet row. It writes the URL of the generated 3D model into the "IMAGE RESULT" column for the corresponding row_number, thus marking the task as complete.

Set Up Steps

To configure this workflow in your n8n environment, follow these steps:

  1. Prepare the Google Sheet:

    • Clone the provided Google Sheet template.
    • Insert the URLs of the model images you want to convert into the "IMAGE MODEL" column.
    • Leave the "IMAGE RESULT" column empty; it will be populated automatically.
    • In n8n, set up a "Google Sheets OAuth2 API" credential and connect it to the Get new image and Update result nodes. Ensure the documentId points to your cloned sheet.

  2. Configure the FAL.AI API Connection:

    • Create an account at fal.ai and obtain your API key.
    • In n8n, create an "HTTP Header Auth" credential. Set the Header Name to Authorization and the Header Value to Key YOUR_API_KEY_HERE (replace with your actual key).
    • Apply this credential to the following nodes: Create 3D Image, Get status, and Get Url 3D image.

  3. Verify Workflow Logic (Key Nodes):

    • Get new image: Confirm the filtersUI is set to look for empty rows in the correct column (e.g., "3D RESULT" or "IMAGE RESULT").
    • Create 3D Image: Verify the JSON body correctly references the image URL from the previous node ({{ $json.image }}).
    • Completed? (If node): Ensure the condition checks for the string COMPLETED from {{ $json.status }}.
    • Update result: Double-check that the column mapping correctly uses row_number to match the row and updates the "IMAGE RESULT" column with the GLB URL

  4. Activate & Test:

    • Save the workflow.
    • Use the When clicking ‘Test workflow’ node for an initial manual test with one image URL in your sheet.
    • Once confirmed working, you can enable the Schedule Trigger node for automatic, periodic execution.

Need help customizing?

Contact me for consulting and support or add me on Linkedin.