MoMask - Generative Masked Modeling of 3D Human Motions
Product Information
Key Features of MoMask - Generative Masked Modeling of 3D Human Motions
MoMask utilizes a hierarchical quantization scheme to represent human motion as multi-layer discrete motion tokens with high-fidelity details, outperforming state-of-art methods on the text-to-motion generation task.
Hierarchical Quantization Scheme
Represents human motion as multi-layer discrete motion tokens with high-fidelity details.
Masked Transformer
Predicts randomly masked motion tokens conditioned on text input at training stage.
Residual Transformer
Learns to progressively predict the next-layer tokens based on the results from current layer.
Text-Driven Motion Generation
Generates 3D human motions based on text input, leveraging the hierarchical quantization scheme.
Temporal Inpainting
Inpaints specific regions within existing motion clips, conditioned on a textual description.
Use Cases of MoMask - Generative Masked Modeling of 3D Human Motions
Text-driven 3D human motion generation
Temporal inpainting of motion clips
Motion generation for animation and video games
Human-computer interaction and robotics
Pros and Cons of MoMask - Generative Masked Modeling of 3D Human Motions
Pros
- Outperforms state-of-art methods on the text-to-motion generation task
- Generates high-fidelity motion representation
- Can be applied to related tasks without further model fine-tuning
Cons
- May require significant computational resources
- Limited to specific domains or applications
How to Use MoMask - Generative Masked Modeling of 3D Human Motions
- 1
Use MoMask for text-driven 3D human motion generation
- 2
Apply MoMask to related tasks such as temporal inpainting
- 3
Fine-tune MoMask for specific domains or applications