HumanMAC: Masked Motion Completion for Human Motion Prediction (2302.03665v4)

Abstract: Human motion prediction is a classical problem in computer vision and computer graphics, which has a wide range of practical applications. Previous effects achieve great empirical performance based on an encoding-decoding style. The methods of this style work by first encoding previous motions to latent representations and then decoding the latent representations into predicted motions. However, in practice, they are still unsatisfactory due to several issues, including complicated loss constraints, cumbersome training processes, and scarce switch of different categories of motions in prediction. In this paper, to address the above issues, we jump out of the foregoing style and propose a novel framework from a new perspective. Specifically, our framework works in a masked completion fashion. In the training stage, we learn a motion diffusion model that generates motions from random noise. In the inference stage, with a denoising procedure, we make motion prediction conditioning on observed motions to output more continuous and controllable predictions. The proposed framework enjoys promising algorithmic properties, which only needs one loss in optimization and is trained in an end-to-end manner. Additionally, it accomplishes the switch of different categories of motions effectively, which is significant in realistic tasks, e.g., the animation task. Comprehensive experiments on benchmarks confirm the superiority of the proposed framework. The project page is available at https://lhchen.top/Human-MAC.

• The paper introduces a masked motion completion framework that replaces traditional encoding-decoding methods with diffusion models.
• It simplifies training by using a single loss function, reducing hyper-parameter dependency and streamlining the prediction process.
• Evaluation on benchmarks like Human3.6M shows HumanMAC generates diverse, physically plausible motions that outperform state-of-the-art methods.

An Overview of HumanMAC: Masked Motion Completion for Human Motion Prediction

The paper "HumanMAC: Masked Motion Completion for Human Motion Prediction" addresses the enduring issue of human motion prediction (HMP) within computer vision and graphics, proposing a novel method distinct from conventional paradigms. This work contributes a framework grounded in masked motion completion, leveraging diffusion models to progress beyond the limitations of encoding-decoding methodologies traditionally employed in motion prediction.

Key Contributions and Methodology

The core innovation of this research lies in utilizing a masked completion strategy, departing from the prevalent encoding-decoding style, which is often encumbered by complex training processes, multiple loss constraints, and limited ability to handle diverse motion categories. The authors introduce HumanMAC, a framework that combines observed and predicted motions using a motion diffusion model trained to generate realistic motion sequences from noise.

1. Masked Completion Framework: Unlike traditional approaches, HumanMAC frames motion prediction as a masked completion task. During inference, it uses a process termed DCT-Completion, which integrates Discrete Cosine Transform (DCT) to condition predictions on observed motions, thereby ensuring continuity and control over the predicted motions.
2. Simplified Training: The framework is optimized using a single loss function within an end-to-end training paradigm, significantly simplifying the training pipeline compared to multi-stage methodologies. This aspect reduces the dependency on meticulously crafted hyper-parameters and multiple loss terms.
3. Diverse Motion Prediction: By modeling the entire sequence holistically, HumanMAC effectively captures the switch between diverse motion categories, addressing the critical need for diversity in realistic tasks such as animation.

Evaluation and Results

Empirical validation on benchmarks like Human3.6M and HumanEva-I demonstrates the framework's superior performance over state-of-the-art methods. Notably, HumanMAC achieves competitive outcomes in terms of Accuracy (ADE and FDE metrics) and diversity (APD metric) while maintaining physical plausibility in the predictions.

• Quantitative Metrics: HumanMAC shows robust performance, particularly in minimizing ADE and FDE metrics, which signify accurate predictive modeling of human biomechanics.
• Qualitative Analysis: Visual comparisons reveal that HumanMAC generates more plausible motion trajectories without excessive jitter or unrealistic outcomes found in benchmark methods such as GSPS and DLow.

Implications and Future Prospects

The HumanMAC framework represents a significant step in HMP research, providing a compelling alternative to encoding-decoding strategies. Its capacity for improved control and transition between motion categories suggests expansive applications in animation and virtual reality. Furthermore, the framework's adaptability to different datasets highlights its potential for real-world applications and cross-domain generalization.

Future research could explore the framework's application in more complex, real-time prediction environments. There's room to reduce the computational intensity of the diffusion steps further, potentially exploring advanced algorithms like DPM-solver++ for efficiency gains. Moreover, expanding the evaluation to larger, more diverse datasets would offer insights into the framework's scalability and robustness in broader contexts.

The paper sets a precedent in leveraging diffusion models for masked completion, which could inspire advancements in other sequential prediction tasks within AI, promoting continuity, control, and diversity in predictive modeling.