Aligned and realistic latent diffusion for text-to-motion generation

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Text-to-motion generation synthesizes human motion from text descriptions.
• Diffusion models offer advantages for continuous motion but often produce over-smoothed results due to mean squared error loss.
• Existing methods struggle to capture semantic nuances and fine details in motion synthesis.

Purpose of the Study:

• To propose an enhanced latent-space diffusion framework for text-driven motion synthesis.
• To improve distribution approximation and semantic alignment in continuous diffusion models.
• To overcome limitations of mean squared error loss in current text-to-motion generation techniques.

Main Methods:

• Incorporated a latent-space adversarial discriminator with decoupled adversarial supervision.
• Introduced a latent-space contrastive alignment strategy during the denoising process.
• Utilized explicit cross-modal constraints for enhanced semantic alignment.

Main Results:

• Mitigated detail loss and enhanced physical realism and dynamic sharpness of generated motions.
• Significantly improved correspondence between generated motion sequences and textual descriptions.
• Demonstrated superior performance over conventional diffusion models on standard benchmarks.

Conclusions:

• The proposed continuous diffusion framework effectively addresses limitations of existing text-to-motion models.
• The enhanced framework shows significant potential for high-fidelity, semantically accurate motion synthesis.
• Validated the benefits of adversarial and contrastive strategies in latent-space diffusion for motion generation.