MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model
(2404.19759)Abstract
This work introduces MotionLCM, extending controllable motion generation to a real-time level. Existing methods for spatial control in text-conditioned motion generation suffer from significant runtime inefficiency. To address this issue, we first propose the motion latent consistency model (MotionLCM) for motion generation, building upon the latent diffusion model (MLD). By employing one-step (or few-step) inference, we further improve the runtime efficiency of the motion latent diffusion model for motion generation. To ensure effective controllability, we incorporate a motion ControlNet within the latent space of MotionLCM and enable explicit control signals (e.g., pelvis trajectory) in the vanilla motion space to control the generation process directly, similar to controlling other latent-free diffusion models for motion generation. By employing these techniques, our approach can generate human motions with text and control signals in real-time. Experimental results demonstrate the remarkable generation and controlling capabilities of MotionLCM while maintaining real-time runtime efficiency.
Overview
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MotionLCM, standing for Motion Latent Consistency Model, is designed to generate human motions from text descriptors in real-time, reducing inference times to approximately 30 milliseconds per motion sequence.
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The model utilizes latent consistency distillation in a compressed space for efficient processing and incorporates Motion ControlNet for enhanced control over the generated motions.
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Despite its rapid generation capabilities, MotionLCM doesn't compromise on the quality of the motion sequences, making it suitable for real-time applications such as VR, gaming, and live animations.
Exploring MotionLCM: Enhancing Real-Time Text-to-Motion Synthesis
Understanding MotionLCM
MotionLCM, short for Motion Latent Consistency Model, is a new model developed to address the computational challenges in generating human motions from textual descriptions in real-time. Traditional text-to-motion models often suffer from long inference times, making them impractical for real-time applications. MotionLCM tackles this by implementing a latent consistency model specifically adapted for motion synthesis, which remarkably cuts down the inference time to approximately 30 milliseconds per motion sequence.
Key Components of MotionLCM
MotionLCM is built on the foundation of a latent diffusion model but focuses on improving two main aspects: efficiency and control.
Efficiency through Latent Consistency Distillation:
MotionLCM utilizes a technique known as latent consistency distillation, where it processes motion data in a compressed latent space rather than operating directly on high-dimensional motion data. This approach significantly reduces the computational load, enabling the model to generate motions in a fraction of the time it takes traditional models.
Control with Motion ControlNet:
To enhance the control over generated motions, MotionLCM incorporates a component called Motion ControlNet. This network operates within the latent space, guiding the generation process using specific control signals like pelvis trajectory. This setup allows for detailed manipulation of the generated motion, adhering closely to the given textual and control inputs.
Performance and Results
The paper presents comprehensive experiments demonstrating that MotionLCM not only achieves superior runtime efficiency but also maintains high quality in the generated motion sequences. Particularly notable results include:
Inference Speed:
MotionLCM generates motions significantly faster (around 30ms per sequence) compared to existing models like MDM and MLD, which require seconds to minutes for similar tasks.
Quality and Control:
Experiment results show that MotionLCM can still produce high-quality motions that closely follow the provided text descriptions and control signals. It effectively balances fast generation times without a substantial sacrifice in motion quality.
Practical Implications and Future Prospects
Practical Applications: With its real-time performance, MotionLCM can be extremely useful in various interactive and real-time systems such as virtual reality (VR), gaming, and live animations where rapid generation of human-like motions from textual cues is necessary.
Future Development: While MotionLCM marks a significant improvement in text-to-motion synthesis, there are areas for enhancement, such as improving the model's performance in motion control tasks to the levels of guided diffusion models. Further research could also explore reducing the physical implausibility of generated motions and handling noisy or anomalous data more effectively.
Conclusion
MotionLCM offers an innovative solution to the long-standing challenge of efficiently generating controlled human motion from text. Its ability to perform in real-time without considerable quality trade-offs holds promising potential for future applications in technology-driven industries requiring immediate motion generation. As the field progresses, optimizing these models for even greater control and efficiency will continue to be a key focus.
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