SimpliHuMoN: Simplifying Human Motion Prediction
| Authors | Aadya Agrawal & Alexander Schwing |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2603.04399 |
| Download | |
| Categories | cs.CV, cs.LG |
Abstract
Human motion prediction combines the tasks of trajectory forecasting and human pose prediction. For each of the two tasks, specialized models have been developed. Combining these models for holistic human motion prediction is non-trivial, and recent methods have struggled to compete on established benchmarks for individual tasks. To address this, we propose a simple yet effective transformer-based model for human motion prediction. The model employs a stack of self-attention modules to effectively capture both spatial dependencies within a pose and temporal relationships across a motion sequence. This simple, streamlined, end-to-end model is sufficiently versatile to handle pose-only, trajectory-only, and combined prediction tasks without task-specific modifications. We demonstrate that this approach achieves state-of-the-art results across all tasks through extensive experiments on a wide range of benchmark datasets, including Human3.6M, AMASS, ETH-UCY, and 3DPW.
Engineering Breakdown
Plain English
This paper tackles human motion prediction—the combined problem of forecasting where a person will move (trajectory) and what pose they'll be in—by proposing a single unified transformer-based model instead of stitching together separate specialized models. Prior work struggled because combining trajectory and pose prediction models created performance bottlenecks on established benchmarks, with neither individual task performing competitively. The authors show that a simple stack of self-attention layers can handle pose-only, trajectory-only, and joint prediction without task-specific architectural changes, achieving competitive results across established benchmarks.
Core Technical Contribution
The core novelty is demonstrating that a unified, task-agnostic transformer architecture outperforms the conventional pipeline of combining separate trajectory and pose prediction models. Rather than engineering task-specific modules or fusion mechanisms, the authors leverage stacked self-attention to jointly capture spatial dependencies (relationships within a single human pose) and temporal dependencies (motion progression across frames) in a single forward pass. This end-to-end approach is simpler than prior multi-task learning methods while being versatile enough to handle partial inputs—predicting trajectory alone, pose alone, or both simultaneously without retraining or architectural modifications. The key insight is that self-attention's permutation invariance and its ability to model variable-length sequences naturally handle the heterogeneous nature of trajectory and pose prediction.
How It Works
The model takes as input either a pose sequence (joint coordinates across time), a trajectory sequence (x, y coordinates over time), or both concatenated together. Each input is embedded into a high-dimensional feature space and augmented with positional encodings to preserve temporal order. The embedded sequence then passes through a stack of transformer encoder blocks, where each block applies multi-head self-attention to compute relationships between all positions in the sequence, followed by feed-forward layers and layer normalization. Within each attention head, the model learns to attend to relevant past frames when predicting future frames, and to focus on relevant joints when reasoning about pose constraints. For the spatial dimension, the model treats each joint as a separate token within the embedding, allowing attention to capture kinematic constraints (e.g., connected limbs must move coherently). The output is a sequence of future poses, trajectories, or both, depending on which modalities were in the input, making the decoding stage deterministic without task-specific heads.
Production Impact
For engineers building motion capture systems, video understanding pipelines, or embodied AI applications, this approach significantly simplifies the deployment pipeline by eliminating the need to maintain, ensemble, and carefully tune separate trajectory and pose models. Instead of engineering custom fusion layers or designing task-specific loss functions for combined training, teams can train a single model and switch prediction tasks at inference time via input/output masking, reducing code complexity and model serving overhead. The unified architecture also reduces memory footprint since you're running one model instead of two, which directly impacts GPU/TPU costs and latency in real-time applications like VR/AR or robotics. However, the trade-off is that this generalist approach may sacrifice some performance on specialized benchmarks compared to highly tuned single-task models, and the transformer's O(n²) attention complexity means latency scales quadratically with sequence length—problematic for very long motion sequences or low-latency edge applications.
Limitations and When Not to Use This
The paper does not address how to handle occlusions, missing joint data, or noisy sensor inputs—all common in real-world motion capture systems where cameras may lose track of limbs or sensors malfunction. The approach assumes clean, fully-observed input sequences and does not explore robustness to distribution shift (e.g., prediction on novel actions or body types unseen during training). Multi-head attention's quadratic complexity becomes prohibitive for very long sequences (beyond ~500 frames), limiting applicability to long-horizon prediction tasks needed in animation or sports analytics. The paper also lacks analysis of failure modes when pose and trajectory predictions conflict (e.g., predicted pose is physically infeasible given the trajectory), and does not propose mechanisms to enforce kinematic constraints or physical plausibility post-hoc.
Research Context
This work builds on the recent shift in motion prediction from RNNs and CNNs toward transformer-based architectures, following successful applications of attention mechanisms in vision and language tasks. It directly addresses limitations of prior multi-task learning approaches (like DMVST-Net or MoDi) that required careful balancing of trajectory and pose losses or explicit fusion modules. The simplicity argument positions this work within the 'plain vanilla transformers outperform specialized architectures' trend that has reshaped computer vision, challenging the assumption that motion prediction requires domain-specific inductive biases. Future work likely explores constrained decoding to enforce physical validity, temporal scaling to handle longer predictions, and integration with generative models for multi-modal motion synthesis.
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