5 papers
IAM: Identity-Aware Human Motion and Shape Joint Generation
Wenqi Jia, Zekun Li, Abhay Mittal +6
Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches…
UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors
Xiaoyan Cong, Zekun Li, Zhiyang Dou +9
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets…
LLaMo: Scaling Pretrained Language Models for Unified Motion Understanding and Generation with Continuous Autoregressive Tokens
Zekun Li, Sizhe An, Chengcheng Tang +7
Recent progress in large models has led to significant advances in unified multimodal generation and understanding. However, the development of models that unify motion-language ge…
PHD: Personalized 3D Human Body Fitting with Point Diffusion
Hsuan-I Ho, Chen Guo, Po-Chen Wu +6
We introduce PHD, a novel approach for personalized 3D human mesh recovery (HMR) and body fitting that leverages user-specific shape information to improve pose estimation accuracy…
Memory-efficient Streaming VideoLLMs for Real-time Procedural Video Understanding
Dibyadip Chatterjee, Edoardo Remelli, Yale Song +9
We introduce ProVideLLM, an end-to-end framework for real-time procedural video understanding. ProVideLLM integrates a multimodal cache configured to store two types of tokens - ve…