Single Motion Diffusion (2302.05905v2)

Abstract: Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging domain, which is rich with large available datasets, the number of data instances for the motion domain is limited, particularly for the animation of animals and exotic creatures (e.g., dragons), which have unique skeletons and motion patterns. In this work, we present a Single Motion Diffusion Model, dubbed SinMDM, a model designed to learn the internal motifs of a single motion sequence with arbitrary topology and synthesize motions of arbitrary length that are faithful to them. We harness the power of diffusion models and present a denoising network explicitly designed for the task of learning from a single input motion. SinMDM is designed to be a lightweight architecture, which avoids overfitting by using a shallow network with local attention layers that narrow the receptive field and encourage motion diversity. SinMDM can be applied in various contexts, including spatial and temporal in-betweening, motion expansion, style transfer, and crowd animation. Our results show that SinMDM outperforms existing methods both in quality and time-space efficiency. Moreover, while current approaches require additional training for different applications, our work facilitates these applications at inference time. Our code and trained models are available at https://sinmdm.github.io/SinMDM-page.

Single Motion Diffusion: A Detailed Examination

The paper presents "Single Motion Diffusion Model" (SinMDM), an innovative framework aimed at synthesizing animations from single motion sequences using diffusion models. This work specifically targets domains where extensive motion datasets are unavailable, such as animations involving animals or fictional creatures with unique skeletal structures and motion patterns.

Overview of SinMDM

SinMDM is designed to address the challenge of learning from a single motion instance, drawing inspiration from diffusion models traditionally used in image synthesis. The model introduces a denoising network tailored to capture the internal motifs of a single motion sequence, enabling it to generate diverse and extensive motion sequences faithfully reflecting the learned patterns.

Key architectural features include:

• Lightweight UNet Architecture: SinMDM employs a shallow UNet, mitigating overfitting through local attention layers that restrict the receptive field, promoting motion diversity.
• Local Attention Mechanism: The use of QnA (Query and Attention) local attention layers replaces global attention mechanisms, allowing for efficient and expressive processing within limited data contexts.
• Broad Application Scope: SinMDM is adaptable for numerous tasks, including spatial and temporal motion in-betweening, style transfer, and crowd animation, all achievable at inference time without retraining.

Strong Numerical Results and Claims

The paper presents robust numerical results, demonstrating that SinMDM outperforms existing models in both quality and time-space efficiency. The authors executed comprehensive experiments using benchmark datasets such as HumanML3D and Mixamo, and metrics highlight superior performance in diversity and fidelity when compared to baseline models like Ganimator and MDM.

Implications and Speculations on Future Developments

Practical Implications: SinMDM provides animators and artists working with non-humanoid motion patterns a valuable tool for generating high-quality, diverse animations without necessitating large datasets. This is particularly advantageous in entertainment and gaming industries, where bespoke motion sequences are often required.

Theoretical Implications: The successful adaptation of diffusion models to single-instance learning challenges the prevailing view that these models require extensive data, opening avenues for their application in other limited-data domains.

Future Developments: Future research could explore extending SinMDM to incorporate sparse datasets from related motion classes, potentially enriching its application scope. Additionally, efforts to optimize the slower inference time of diffusion models remain a fertile area for further investigation.

In conclusion, this paper presents a significant contribution to motion synthesis by leveraging diffusion models for single-instance learning, paving the way for new methodologies in AI-guided animation generation. The SinMDM model stands as a testament to the efficacy of combining narrow receptive fields and local attention mechanisms in handling data-scarce environments.