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.
Exploring the limits of transfer learning with a unified text-to-text transformer
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Fisher information geometry supplies parameterization-invariant metrics for MoE specialization dynamics and early failure prediction with strong empirical correlations.
This is the first survey on vision-language-action models, providing a taxonomy across three lines, plus summaries of datasets, simulators, benchmarks, challenges, and future directions in embodied AI.
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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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Geometric Metrics for MoE Specialization: From Fisher Information to Early Failure Detection
Fisher information geometry supplies parameterization-invariant metrics for MoE specialization dynamics and early failure prediction with strong empirical correlations.
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A Survey on Vision-Language-Action Models for Embodied AI
This is the first survey on vision-language-action models, providing a taxonomy across three lines, plus summaries of datasets, simulators, benchmarks, challenges, and future directions in embodied AI.