adaptive autoencoder 1human motion generation 1motion-language grounding 1retrieval evaluation 1text-to-motion 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
MUGEN: A Unified Framework for Efficient Motion Understanding and Generation
Zhankai Ye, Yukai Jin, Bingyang Wei +5
The paper introduces MUGEN, a unified framework that uses a single adaptive-length autoencoder to compress human motion into continuous latent slots, enabling efficient text-to-mot…
cs.CV2026
GeoMotionGPT: Geometry-Aligned Motion Understanding with Large Language Models
Zhankai Ye, Bofan Li, Yukai Jin +5
Discrete motion tokenization has recently enabled Large Language Models (LLMs) to serve as versatile backbones for motion understanding and motion-language reasoning. However, exis…
cs.CL2025
HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks
Yiming Zeng, Jinghan Cao, Zexin Li +7
Instruction-based text editing is increasingly critical for real-world applications such as code editors (e.g., Cursor), but Large Language Models (LLMs) continue to struggle with…