Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis (1707.05363v5)

Abstract: We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.

• The paper introduces acRNN, an auto-conditioned recurrent network that addresses error accumulation for reliable long-term human motion synthesis.
• The methodology uses scheduled sampling to integrate predicted outputs back as inputs, enabling the generation of motion sequences exceeding 18,000 frames.
• The results demonstrate realistic synthesis across diverse motion styles, offering practical applications in animation, gaming, and virtual reality.

Analytical Overview of "Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis"

The paper "Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis" introduces an innovative approach to generating complex human motion sequences. The core contribution involves the development of the auto-conditioned Recurrent Neural Network (acRNN), designed to handle error accumulation typically seen in autoregressive models for motion synthesis. This approach allows the synthesis of prolonged sequences of complex human motions without relying on databases, overcoming limitations such as sequence freezing or divergence prevalent in prior methods.

Technical Contributions

The paper identifies and addresses the primary challenge of autoregressive frameworks - the accumulation of errors. Traditional models, when recursively using predicted outputs as input, often deviate from realistic motion after a few seconds due to discrepancies between training and test-time data distributions. To mitigate this, the acRNN structures the training process to integrate its outputs back as inputs at scheduled intervals, conditioning the model to handle potentially erroneous data more effectively. This mirrors the concept of "scheduled sampling" but is implemented in a systematic manner rather than stochastically.

In addition to extending prior work on human motion synthesis, this paper distinguishes itself by demonstrating the generation of extended sequences, including over 18,000 frames, of diverse and multifaceted styles such as various dance forms and martial arts. The acRNN allows for learning over these styles without pre-defined databases, crucial for applications requiring realistic and varied virtual human movements.

Results and Evaluation

Quantitative assessments highlight that the acRNN avoids freezing for hundreds of seconds, unlike conventional frameworks which fail within a couple of seconds. It shows favorable performance in motion prediction accuracy across different complex styles, suggesting its potential practical applicability for animators and game developers requiring long-term motion generation.

Qualitatively, results from the acRNN demonstrate realistic and sustained dynamics across multiple motion styles. The paper provides examples of synthesized sequences that do not merely mimic learned sequences but generate new, plausible variations independently, showcasing the network's ability to capture and generate the essence of the diverse styles it learned.

Implications and Future Directions

The acRNN introduces significant implications for motion simulation and animation. Its ability to generate lengthy, realistic sequences without database dependency positions it as a vital tool for content creators in multimedia, film, and gaming industries, potentially enhancing autonomous virtual reality environments.

While the paper advances the field of motion synthesis, the authors acknowledge areas for future development such as addressing artifacts and refining model stability to ensure perpetual generation fidelity. Future exploration could delve into integrating acRNN with physics-based simulations for even more nuanced motion realism. Additionally, the exploration of condition lengths’ impact on both prediction precision and output variability could foster deeper understanding and refinement of the training regime.

Overall, acRNN establishes a novel paradigm in motion synthesis, promising robustness and adaptability far exceeding previous approaches. The frameworks and findings elucidated in this research mark an evolution in leveraging recurrent networks for complex motion tasks, setting a foundation for subsequent innovation in dynamic human modeling.