[Paper] SimpliHuMoN: Simplifying Human Motion Prediction

Source: arXiv - 2603.04399v1

Overview

The paper “SimpliHuMoN: Simplifying Human Motion Prediction” introduces a single, transformer‑based architecture that can predict human poses, trajectories, or both at the same time. By replacing a patchwork of task‑specific networks with one clean model, the authors achieve new state‑of‑the‑art results on several widely used benchmarks, showing that simplicity can win over complexity in this domain.

Key Contributions

• Unified Transformer Model – A single end‑to‑end network that handles pose‑only, trajectory‑only, and combined motion prediction without any architectural tweaks.
• Self‑Attention for Spatial & Temporal Modeling – Stacked self‑attention layers simultaneously capture joint‑level dependencies within a frame and temporal dynamics across frames.
• State‑of‑the‑Art Performance – Sets new best results on Human3.6M, AMASS, ETH‑UCY, and 3DPW, outperforming specialized baselines for each sub‑task.
• Simplicity & Efficiency – Fewer hyper‑parameters and training pipelines compared to prior multi‑module systems, making it easier to reproduce and extend.
• Extensive Empirical Validation – Ablation studies and cross‑dataset experiments demonstrate robustness and generalization.

Methodology

The core of SimpliHuMoN is a standard transformer encoder consisting of several identical self‑attention blocks:

1. Input Representation – Each time step is encoded as a flattened vector of joint coordinates (for pose) and/or 2‑D/3‑D position of the root joint (for trajectory). Positional encodings inject temporal order.
2. Spatial Self‑Attention – Within a single frame, attention lets the model learn how the movement of one joint influences another (e.g., elbow ↔ wrist).
3. Temporal Self‑Attention – Across frames, attention captures long‑range dependencies such as the swing of a leg affecting future arm motion.
4. Stacked Layers – Multiple attention layers deepen the receptive field, enabling the network to model both short‑term dynamics and longer‑term intent.
5. Prediction Head – A lightweight linear projection maps the final transformer embeddings back to the desired output format (pose, trajectory, or both).

Training uses a simple mean squared error loss on the predicted joint/position coordinates, optionally combined with a velocity regularizer to encourage smoothness. No task‑specific loss weighting or auxiliary networks are required.

Results & Findings

• The model consistently beats specialized baselines by 5‑10 % on average.
• Ablation experiments show that removing either spatial or temporal attention degrades performance by ~8 %, confirming the importance of both components.
• Training time per epoch is comparable to, or slightly lower than, the most efficient prior methods because the architecture avoids multiple sub‑networks.

Practical Implications

• Game Development & Animation – Studios can integrate a single model to generate realistic character motion from sparse inputs (e.g., only foot positions), reducing pipeline complexity.
• Robotics & Human‑Robot Interaction – Predicting both where a person will walk and how their limbs will move enables safer, more anticipatory robot planning.
• AR/VR Avatars – Real‑time pose and trajectory prediction from head‑mounted sensors becomes feasible with a lightweight transformer, improving avatar fidelity without heavy compute.
• Surveillance & Autonomous Driving – Unified motion forecasting can feed directly into intent‑prediction modules, simplifying data handling and improving prediction consistency across pedestrians and cyclists.
• Research & Prototyping – The open‑source‑friendly design lowers the barrier for experimenting with multimodal motion data, encouraging cross‑task innovations.

Limitations & Future Work

• Data Hunger – Like most transformers, SimpliHuMoN benefits from large, diverse motion capture datasets; performance may drop on niche motions with limited examples.
• Real‑Time Constraints – While efficient, the model still requires GPU acceleration for low‑latency inference, which could be a bottleneck on edge devices.
• Physical Plausibility – The loss is purely geometric; incorporating physics‑based constraints (e.g., contact forces) could further improve realism.
• Multi‑Agent Scenarios – Extending the architecture to jointly predict interactions among several agents is left as an open challenge.

The authors suggest exploring lightweight attention variants, integrating biomechanical priors, and scaling to collaborative motion datasets as next steps.

Authors

• Aadya Agrawal
• Alexander Schwing

Paper Information

• arXiv ID: 2603.04399v1
• Categories: cs.CV, cs.LG
• Published: March 4, 2026
• PDF: Download PDF