Executing your Commands via Motion Diffusion in Latent Space (2212.04048v3)

Abstract: We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors. Since human motions are highly diverse and have a property of quite different distribution from conditional modalities, such as textual descriptors in natural languages, it is hard to learn a probabilistic mapping from the desired conditional modality to the human motion sequences. Besides, the raw motion data from the motion capture system might be redundant in sequences and contain noises; directly modeling the joint distribution over the raw motion sequences and conditional modalities would need a heavy computational overhead and might result in artifacts introduced by the captured noises. To learn a better representation of the various human motion sequences, we first design a powerful Variational AutoEncoder (VAE) and arrive at a representative and low-dimensional latent code for a human motion sequence. Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space. Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences.

Overview of "Executing your Commands via Motion Diffusion in Latent Space"

The paper "Executing your Commands via Motion Diffusion in Latent Space" addresses the complex problem of generating human motion sequences in response to various conditional inputs, like textual descriptions or action classes. Unlike previous attempts focusing on mapping raw motion data directly from these conditional inputs, this work proposes an innovative Motion Latent-based Diffusion (MLD) model that operates in a latent space designed via a Variational Autoencoder (VAE). This paradigm not only improves the motion generation quality but also significantly reduces computational overhead.

In tackling the inherent challenge of mapping between highly disparate distributions—such as those between language descriptors and human motion sequences—the authors introduce a latent space approach. The proposed motion VAE effectively captures and encodes the salient features of human motions into a low-dimensional latent space. By leveraging the latent space for diffusion processes, the MLD model sidesteps the inefficiencies and overfitting risks associated with direct raw motion sequence modeling.

Extensive experimental analysis on multiple human motion generation tasks—unconditional generation, text-to-motion, and action-to-motion—demonstrates that the MLD model achieves superior performance in terms of fidelity, diversity, and computational efficiency compared to state-of-the-art methods.

Methodology

The core methodology hinges on a two-part system: a VAE and a latent diffusion model. The VAE is responsible for transforming motion sequences into a latent space that preserves the essence of the original motion data. This is crucial because constructing a realistic motion sequence from noisy and high-dimensional raw motion data is both computationally intensively and prone to artifacts.

After encoding the motion into this latent space, the MLD model employs a diffusion process. By operating in this low-dimensional, noise-robust latent space, the model can learn a more effective probabilistic mapping from conditions like action labels or textual descriptions to motion data. This method not only streamlines the handling of diverse conditional inputs but also accelerates the model training and inference phases by two orders of magnitude.

Strong Numerical Results

The results section exhibits compelling quantitative achievements of the MLD model. On the HumanML3D and KIT datasets, the MLD achieves the lowest Frechet Inception Distance (FID) scores, indicating that the generated motions are closer to real data distributions compared to other models. Remarkably, the model maintains high R Precision scores, showcasing superior text conditional generation accuracy.

Moreover, analysis of computational efficiency reveals that MLD markedly outperforms alternative diffusion models, requiring significantly less time for inference—a critical advantage as real-time and responsive systems become more prevalent in applications such as animation and AR/VR.

Implications and Future Directions

The practical implications of this research extend across interactive virtual environments, autonomous robotics, and entertainment industries, where generating realistic human motion efficiently is of paramount importance. Theoretically, it emphasizes the potential of latent space modeling coupled with diffusion processes, paving the way for further developments in generative models for complex high-dimensional data tasks.

Future research could explore the extension of this framework to other types of motion or more diverse conditional inputs, potentially involving more intricate cross-modal interactions. Additionally, investigating the balance between the complexity of the latent space and the model's interpretability or control could yield further improvements in generative quality and computational optimization.

The convergence of latent space techniques and diffusion processes as demonstrated in this paper may inspire new methods and applications in AI, particularly in domains requiring dynamic and contextually-aware generative capabilities.