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Convolutional Sequence to Sequence Model for Human Dynamics
Chen Li, Zhen Zhang, Wee Sun Lee, Gim Hee Lee
TL;DR
Human motion prediction must handle complex biomechanical dynamics, high-dimensional sequences, and spatial coordination that recurrent models may represent imperfectly over long horizons. This paper uses hierarchical convolutional encoders and a decoder to combine long-term and short-term information. On Human3.6M and CMU Motion Capture, the model produces more realistic long-term predictions and outperforms state-of-the-art RNN-based methods.
Problem
Human motion prediction involves complicated dynamics and spatial-temporal correlations, while RNNs can lose long-term information, accumulate errors, and generate mean-pose or unrealistic predictions.
Method
A convolutional sequence-to-sequence model uses long-term and short-term encoders with a spatial decoder to combine distant, recent, and coordinated motion information.
Results
The model outperforms state-of-the-art RNN-based methods on Human3.6M and CMU, especially for long-term prediction, with more realistic complex-action predictions.
Takeaways & Limitations
Hierarchical convolutional structure captures complicated spatial-temporal correlations while preserving both long-term invariant and short-term dynamic information.
Takeaways & Limitations
The paper identifies RNN difficulty in learning long-term correlations and spatial coordination as the limitation motivating its approach.
Abstract
from arXiv · showhide
Human motion modeling is a classic problem in computer vision and graphics. Challenges in modeling human motion include high dimensional prediction as well as extremely complicated dynamics.We present a novel approach to human motion modeling based on convolutional neural networks (CNN). The hierarchical structure of CNN makes it capable of capturing both spatial and temporal correlations effectively. In our proposed approach,a convolutional long-term encoder is used to encode the whole given motion sequence into a long-term hidden variable, which is used with a decoder to predict the remainder of the sequence. The decoder itself also has an encoder-decoder structure, in which the short-term encoder encodes a shorter sequence to a short-term hidden variable, and the spatial decoder maps the long and short-term hidden variable to motion predictions. By using such a model, we are able to capture both invariant and dynamic information of human motion, which results in more accurate predictions. Experiments show that our algorithm outperforms the state-of-the-art methods on the Human3.6M and CMU Motion Capture datasets. Our code is available at the project website.
1. Introduction
Human motion prediction is difficult because human dynamics are complex and existing recurrent models can lose long-term information, converge to mean poses, and miss spatial coordination. The paper proposes a convolutional sequence-to-sequence model that improves realism and accuracy on Human3.6M and CMU datasets.
- Human motion modeling supports collision avoidance, sports analysis, and medical diagnosis, especially where systems interact with people.
- Analytic biomechanical models cover only a few simple actions, so complicated actions require data-driven methods trained on motion-capture data.
- RNN predictors can converge to a static mean pose during long-term prediction because distant information is difficult to retain.
- RNNs may accumulate encoder and decoder errors over long horizons when they fail to learn human motion constraints accurately.
- RNN-based methods struggle to model spatial correlations among joints, producing unrealistic coordination despite strong accuracy performance.
- The proposed convolutional sequence-to-sequence model better avoids long-term mean poses, produces more realistic predictions, and outperforms state-of-the-art methods on Human3.6M and CMU.
2. Related Works
Prior human-motion methods include probabilistic, dynamical, restricted-Boltzmann, recurrent, and convolutional sequence-to-sequence models. The paper targets human motion's combined spatial-temporal dynamics, which distinguish it from primarily temporal machine-translation tasks.
- Data-driven human-motion modeling must address high dimensionality, nonlinear dynamics, and movement uncertainty while balancing model capacity against inference complexity.
- Earlier approaches included Hidden Markov Models, linear dynamical systems, Gaussian-process latent-variable models, and conditional restricted Boltzmann machines.
- RNN-based predictors commonly use encoder-decoder structures that encode observed frames into hidden variables before generating future motion.
- Convolutional sequence-to-sequence models encode seed sequences and decode target sequences, with later methods becoming fully convolutional.
- Unlike machine translation's temporal correlations, human motion requires modeling complicated spatial-temporal dynamics across body joints.
3. Network Architecture
The model uses hierarchical convolutions to capture spatial and long-term temporal dependencies, combining long-term invariant information with short-term dynamics for recursive human-motion prediction. Its architecture preserves long-term context while expanding temporal and spatial interaction ranges.
- Convolutional sequence-to-sequence model: The convolutional architecture hierarchically captures nearby-frame dependencies in lower layers and distant-frame dependencies in higher layers.Stride-two convolutions allow elements distance n apart to interact through O(log(n)) operations, rather than O(n) recurrent steps.
- Convolutional sequence-to-sequence model: A long-term encoder maps the complete observed motion sequence to a hidden variable representing information such as action category, body properties, and environmental constraints.The long-term hidden variable is retained during decoding.
- Convolutional sequence-to-sequence model: The decoder combines a short-term encoder for the most recent C frames with a spatial decoder that predicts the next pose.The short-term encoder uses a sliding window, and the two hidden variables are concatenated before spatial decoding.
- Convolutional sequence-to-sequence model: The short-term encoder and decoder slide forward recursively after each generated frame to produce the full predicted sequence.This design captures short-term dynamical information alongside long-term invariant information.
- Implementation details: The convolutional encoding module uses three convolutional layers and one fully connected layer, while the spatial decoder uses a rectangular 2 × 7 kernel and fully connected layers.The convolutional channels are 64, 128, and 128, followed by a 512-dimensional fully connected representation.
- Optimization: Training uses mean squared error together with dropout, ℓ2, and adversarial regularization, with the discriminator encouraging realistic generated sequences.The short-term window size C trades prediction accuracy against computation, and its effect is evaluated experimentally.
4. Experiments
Experiments compare the convolutional model with recurrent baselines on Human3.6M and CMU Motion Capture, including generalization, qualitative behavior, and ablations. The model generally improves long-term prediction, avoids mean-pose collapse, and benefits from its long-term encoder, spatial kernel, and adversarial regularizer.
- Human3.6M evaluation: The Human3.6M experiments cover 15 actions, with two subject-5 sequences used for testing and the remaining data used for training.The dataset provides accurate 3D joint locations and substantial pose variation across actors.
- Human3.6M evaluation: The model outperforms RRNN in most Human3.6M cases and has stronger average performance for longer-term predictions.On aperiodic actions such as eating, smoking, and discussion, RRNN converges toward a mean pose, whereas the proposed model preserves more plausible motion trends.
- CMU evaluation: On CMU Motion Capture, the general model outperforms RRNN on challenging actions including running and jumping.For running, predictions are close to realistic motion; for jumping, the model captures squatting followed by jumping, with duration as the main error.
- Ablation study: Removing the long-term encoder increases average error, especially at the 1000 ms horizon.The ablation supports the encoder’s role in capturing long-term dependencies.
5. Conclusion
The paper proposes a convolutional sequence-to-sequence model for human motion prediction, using hierarchical convolutional structure to model complex spatial-temporal correlations. It reports better performance than existing state-of-the-art RNN-based models, especially for long-term and complex-action predictions.
- The proposed model uses hierarchical convolutional structure to capture complicated spatial-temporal correlations in human motion.
- The model includes long-term and short-term convolutional encoders so distant and nearby motion information contribute to future prediction.
- The method performs better than existing state-of-the-art RNN-based models, particularly on long-term prediction tasks.