Motion Anything - A multimodal motion generation framework developed by Tencent in collaboration with JD.com and other universities.
Motion Anything is a multimodal motion generation framework developed by institutions such as the Australian National University, the University of Sydney, Tencent, McGill University, and JD.com. It generates high-quality, controllable motion based on text, music, or a combination of both...
What is Motion Anything?
Motion Anything is a multimodal motion generation framework developed by institutions such as the Australian National University, the University of Sydney, Tencent, McGill University, and JD.com. It generates high-quality, controllable human motion based on text, music, or a combination of both. Motion Anything introduces an attention-based mask modeling method to achieve fine-grained control over keyframes and actions in motion sequences, addressing the problem that existing methods cannot prioritize dynamic content generation based on conditions. The framework is based on temporal adaptive and spatial alignment transformers, effectively integrating multiple modal conditions to improve the coherence and diversity of generated motion. It also releases the Text-Music-Dance (TMD) dataset, containing 2153 pairs of paired samples of text, music, and dance, providing a new benchmark for multimodal motion generation research.
Main functions of Motion Anything
- Motion generation under multimodal conditionsGenerate high-quality human motion based on text descriptions, music, or a combination of both.
- Fine-grained controlBased on the attention mechanism, it performs fine-grained control over keyframes and actions of motion to achieve more accurate generation results.
- Dynamic priority adjustmentThe priority of motion generation is dynamically adjusted based on given conditions (such as text or music), prioritizing the generation of dynamic frames and body parts that are most relevant to the conditions.
- Cross-modal alignmentIt effectively aligns text, music, and motion in time and space dimensions, improving the coherence and consistency of generated motion.
The technical principle of Motion Anything
- Attention-based mask modelingThe model uses an attention mechanism to select and mask the motion parts most relevant to the condition (text or music). During generation, the model prioritizes recovering the masked keyframes and actions, thus focusing on dynamic content.
- Time Adaptive TransformerDynamically adjust attention calculations based on the modality of the condition (text, music, or a combination of both). Align motion keyframes with keywords in the text or the beat of the music to improve temporal coherence.
- Spatial alignment transformerRearrange the embedded conditions and motions to expose the spatial dimensions. Restore the masked key actions to ensure spatial consistency between motion and conditions.
- Multimodal conditional codingThis technology simultaneously processes multiple modalities, including text and music, and integrates information from different modalities into motion generation based on an adaptive encoder. This enables controllable motion generation under multimodal conditions, improving the generation quality.
- Dataset supportIt provides a new Text-Music-Dance (TMD) dataset, containing 2153 pairs of text, music, and dance samples, providing rich data support for multimodal motion generation.
Motion Anything's project address
- Project official website:https://steve-zeyu-zhang.github.io/MotionAnything/
- GitHub repository:https://github.com/steve-zeyu-zhang/MotionAnything
- arXiv technical paper:https://arxiv.org/pdf/2503.06955
Application scenarios of Motion Anything
- Film and animationQuickly generate high-quality animations, reduce production costs, and enhance visual effects.
- VR/ARGenerate realistic movements for virtual characters, enhance immersion, and enable natural interaction.
- Game developmentGenerate character movements based on the storyline or music to enhance the game's fun and smoothness.
- Human-computer interactionGenerate natural movements to make robot-human interaction more user-friendly and enable voice-command-driven action generation.
- Education and TrainingGenerate standard movement demonstrations to assist in teaching physical education, dance, and other subjects, thereby improving training effectiveness.