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Recurrent Transition Networks for Character Locomotion (1810.02363v5)

Published 4 Oct 2018 in cs.GR, cs.LG, and stat.ML

Abstract: Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for large games that allow complex and constrained locomotion movements, where the number of transitions grows exponentially with the number of states. In this paper, we present a novel approach, based on deep recurrent neural networks, to automatically generate such transitions given a past context of a few frames and a target character state to reach. We present the Recurrent Transition Network (RTN), based on a modified version of the Long-Short-Term-Memory (LSTM) network, designed specifically for transition generation and trained without any gait, phase, contact or action labels. We further propose a simple yet principled way to initialize the hidden states of the LSTM layer for a given sequence which improves the performance and generalization to new motions. We both quantitatively and qualitatively evaluate our system and show that making the network terrain-aware by adding a local terrain representation to the input yields better performance for rough-terrain navigation on long transitions. Our system produces realistic and fluid transitions that rival the quality of Motion Capture-based ground-truth motions, even before applying any inverse-kinematics postprocess. Direct benefits of our approach could be to accelerate the creation of transition variations for large coverage, or even to entirely replace transition nodes in an animation graph. We further explore applications of this model in a animation super-resolution setting where we temporally decompress animations saved at 1 frame per second and show that the network is able to reconstruct motions that are hard to distinguish from un-compressed locomotion sequences.

Citations (82)

Summary

  • The paper introduces RTN, a deep recurrent architecture that enhances motion transition quality with future-aware conditioning.
  • It employs a modified LSTM with principled hidden state initialization and integrates local terrain data for improved performance.
  • RTN achieves lower mean squared error and centimeter offsets compared to traditional interpolation and earlier RNN models.

Recurrent Transition Networks for Character Locomotion: A Formal Analysis

The paper under review presents an innovative approach to generating transition animations through the application of deep recurrent neural networks (RNNs), specifically tailored for complex human locomotion tasks in video games and animation systems. The proposed model, named Recurrent Transition Network (RTN), is formulated upon a modified Long-Short-Term-Memory (LSTM) network, engineered explicitly for the task of motion transition generation. The research emphasizes the simplification and automation of transition generation without requiring explicit labeling of data, such as gait, phase, or contact points.

Methodology

Central to the paper is the RTN architecture, which advances prior work on Encoder-Recurrent-Decoder (ERD) networks and ResNet RNNs by incorporating future-aware conditioning, optimizing the generation of sequence transitions based on both past context and a defined future state of a character. This is achieved by introducing a principled method for initializing hidden states of the LSTM units, thus enhancing the network's generalization capabilities and reducing error propagation during sequence generation.

The RTN design is augmented with a local terrain representation, allowing the system to accommodate environmental constraints, crucial for improving performance on rough terrain during extended motion transitions. Input sequences are preprocessed to capture key motion dynamics via normalized 3D global positions and velocities, alongside dynamic future context vectors, which include position and velocity information of the target state.

Quantitative Results

The robustness of the RTN is underscored through various empirical evaluations. The network achieves realistic and fluid transitions that are quantitatively competitive with motion capture-based benchmarks, prior to any inverse kinematics postprocessing. Particularly, the RTN demonstrates a mean squared error (MSE) improvement over enhanced baselines like future-aware ERD (F-ERD) and Residual LSTM (F-RESLSTM) architectures. Such findings suggest RTN's capability for accurate and efficient animation transition generation, offering significant reductions in average centimeter offset from ground truth compared to naive interpolation methods.

Implications and Applications

The implications of this research are substantial, with potential to transform animation graph generation in gaming by automating transition generation processes, thereby mitigating the typically labor-intensive manual animation authoring. Beyond animation and gaming, potential applications of RTN span various fields, such as augmented reality, robotic motion planning, and any domain requiring realistic character movements.

Moreover, the paper explores the RTN's application in temporal super-resolution, effectively reconstructing high-quality motion sequences from temporally sparse data, further demonstrating the versatility and scalability of the RTN framework.

Future Directions

While the RTN offers compelling advantages, further research could investigate the integration of bi-directional layers to minimize target blending postprocess without additional computational burden. Additionally, expanding the model's utility to incorporate styles or emotional context could enhance the personalization of character animations. Probabilistic deep learning approaches may also be adopted to introduce mechanisms for uncertainty modeling and multi-modal sampling capabilities.

In conclusion, the RTN presents a robust, scalable, and efficient solution for character motion transition challenges, with promising prospects for broader adoption within animation-centric industries.

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