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Human Motion Prediction

Human Motion Prediction is Python research code that trains recurrent neural networks and running-average baselines to forecast future human motion from Human3.6M sequences.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Human Motion Prediction — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it for research experiments or a reference workflow when you have Human3.6M data in the required format and want to train, evaluate or visualize motion predictions locally.

Who it is for

  • Researchers studying human-motion forecasting with recurrent neural networks.
  • Developers who want to compare model predictions against running-average baselines.
  • Practitioners exploring action-specific prediction and motion-sample visualization.

Documented uses

  • Train a motion-forecasting model for a selected Human3.6M action or across all listed actions.
  • Compare recurrent-network experiments with a running-average baseline.
  • Review action-level Euler-angle error logs in TensorBoard.
  • Save and visualize predicted walking-motion samples.
Before you choose

Requirements & limits

The repository is public and specifies an MIT license.

  • The code requires TensorFlow 1.2 or later; h5py is listed for saving samples.
  • The required Human3.6M data format is exponential map.
  • Training and evaluation are command-line workflows; the README documents TensorBoard logging for action-level Euler-angle error.
  • The repository lists 15 action labels, including walking, sitting and greeting, and supports an all-actions setting.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train and visualize walking-motion samples

Train the walking-action model, save prediction samples and open the resulting visualization.

Before you start

  • The repository cloned locally.
  • Human3.6M data in exponential-map format, placed in the repository's data directory.
  • TensorFlow 1.2 or later and h5py.

Steps

  1. Run the command below to train.

    Documented command
    python src/translate.py --action walking --seq_length_out 25 --iterations 10000

  2. Run the command below to save samples.

    Documented command
    python src/translate.py --action walking --seq_length_out 25 --iterations 10000 --sample --load 10000

  3. Run the command below to visualize the samples.

    Documented command
    python src/forward_kinematics.py

Expected outcome

A visualization of predicted walking motion based on saved model samples.

Guide limitations

  • The README describes this as a quick demo and does not promise a particular result on other hardware.
  • The data must be in the specified Human3.6M exponential-map format.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
una-dinosauria/human-motion-prediction · Official homepage title · retained in its source language
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