Summary of Efficient Text-driven Motion Generation Via Latent Consistency Training, by Mengxian Hu et al.
Efficient Text-driven Motion Generation via Latent Consistency Training
by Mengxian Hu, Minghao Zhu, Xun Zhou, Qingqing Yan, Shu Li, Chengju Liu, Qijun Chen
First submitted to arxiv on: 5 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework, Motion Latent Consistency Training (MLCT), for efficient human motion generation in multimodal human-computer interactions. The existing approaches face efficiency challenges due to the computational overhead of solving reverse diffusion trajectories during inference. MLCT precomputes these trajectories in the training phase and enables few-step or single-step inference using self-consistency constraints. A motion autoencoder with quantization constraints is used for constructing solution distributions, while a classifier-free guidance format is constructed via an additional unconditional loss function to precompute conditional diffusion trajectories. The paper also introduces a clustering guidance module based on K-nearest-neighbor algorithm for chain-conduction optimization of self-consistency constraints. Benchmarks demonstrate that MLCT significantly outperforms traditional consistency distillation methods with reduced training cost and enhanced consistency model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about creating better computer-generated human movements. Currently, computers have trouble generating these movements efficiently because they need to solve a complex math problem each time. The researchers came up with a new way to do this called Motion Latent Consistency Training (MLCT). MLCT solves the math problem once during training and then uses that solution to quickly generate movements later on. This makes it much faster and more efficient. The results show that MLCT is much better than current methods and can even perform as well as the best current models but at a lower cost. |
Keywords
» Artificial intelligence » Autoencoder » Clustering » Diffusion » Distillation » Inference » Loss function » Nearest neighbor » Optimization » Quantization
