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MotionCLR - an AI motion editing model that generates corresponding motion sequences based on text prompts.

MotionCLR is an attention-based model for generating and editing human motion. It can generate actions based on text prompts and supports interactive editing by users, such as emphasizing, weakening, replacing, erasing, and style transfer. MotionCLR...

What is MotionCLR?

MotionCLR is an attention-based human motion generation and editing model that can generate actions based on text prompts and supports interactive editing by users, such as motion emphasis, reduction, replacement, erasure, and style transfer. MotionCLR understands and edits actions based on self-attention and cross-attention mechanisms, supporting diverse motion editing tasks, such as motion emphasis/reduction, in-situ motion replacement, and example-based motion generation. The model performs well on the HumanML3D dataset, generating high-quality actions with accurate alignment to text descriptions, achieving state-of-the-art performance in terms of diversity, interpretability, and editing capabilities.

Main functions of MotionCLR

  • Text-driven action generation: Generate a corresponding action sequence based on the text description provided by the user.
  • Emphasis and reduction of movement: Adjust the weight of action keywords in the text to increase or decrease the generated action.
  • In-place action replacement: It allows users to replace specific actions while keeping other actions unchanged.
  • Example-based action generation: Generate a new sequence of actions similar to a given example action.
  • Style shift: Applying the style of one action to another generates a sequence of actions with new style characteristics.
  • Action sequence editing: It supports more complex editing of action sequences, such as adjusting the order of actions and changing the duration of actions.

The technical principle of MotionCLR

  • Self-attention mechanism: Capture the temporal relationships between frames within a motion sequence to ensure the continuity and naturalness of the motion.
  • Cross-attention mechanism: Establish a fine-grained correspondence between text descriptions and action sequences, so that the generated actions accurately reflect the content of the text.
  • Diffusion model: The generated actions are gradually improved using a diffusion model, making the model more refined and realistic.
  • Training free editors: The model supports action editing without retraining, improving the convenience and practicality of editing.
  • Attention map manipulation:Action editing is achieved by directly manipulating attention maps, such as adjusting attention weights to change the emphasis or reduction of actions.

MotionCLR project address

Application scenarios of MotionCLR

  • Game Development: Generate game character animations, providing natural and diverse character movements.
  • Animation Production: It helps animators quickly generate or modify character movements, improving animation production efficiency.
  • Virtual Reality (VR) and Augmented Reality (AR): Generate realistic user actions for use in VR/AR interactive experiences.
  • Film and television production: Used for pre-visualizing action scenes or providing action references for post-production.
  • Human-computer interaction: In interactive systems that require human motion as input, such as motion capture and game control.
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