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MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning
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MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning
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We introduce MotionRL, the first approach to utilize Multi-Reward Reinforcement Learning (RL) for optimizing text-to-motion generation tasks and aligning them with human preferences. Previous works focused on improving numerical performance metrics on the given datasets, often neglecting the variability and subjectivity of human feedback. In contrast, our novel approach uses reinforcement learning to fine-tune the motion generator based on human preferences prior knowledge of the human perception model, allowing it to generate motions that better align human preferences. In addition, MotionRL introduces a novel multi-objective optimization strategy to approximate Pareto optimality between text adherence, motion quality, and human preferences. Extensive experiments and user studies demonstrate that MotionRL not only allows control over the generated results across different objectives but also significantly enhances performance across these metrics compared to other algorithms.
Forward citations
Cited by 4 Pith papers
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MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
MotionMERGE proposes a multi-granular LLM framework for fine-grained text-driven human motion editing, reasoning, generation, and explanation, supported by the new MotionFineEdit dataset with spatio-temporal annotations.
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SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control
A new diffusion transformer policy with joint attention over actions, states, and text plus RL post-training outperforms prior methods on language alignment and motion quality for humanoid control.
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SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-based Humanoid Control
SCRIPT presents a scalable diffusion policy with JAST-DiT architecture, nonlinear history conditioning, and RLHR post-training that claims to outperform prior methods on text alignment, motion quality, and physical re...
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IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation
Interleaving motion generation with text-motion assessment and refinement improves alignment between generated human motion and goal text.
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