5 papers
DAPE: Dynamic Non-uniform Alignment and Progressive Detail Enhancement Techniques for Improving the Performance of Efficient Visual Language Models
Mengyuan Tian, Qiyan Zhao, Yanan Wang +1
In recent years, pre-trained visual-linguistic models have demonstrated tremendous potential, becoming a crucial foundational framework for numerous downstream tasks. However, the…
ActionPlan: Future-Aware Streaming Motion Synthesis via Frame-Level Action Planning
Eric Nazarenus, Chuqiao Li, Yannan He +3
We present ActionPlan, a unified motion diffusion framework that bridges real-time streaming with high-quality offline generation within a single model. The core idea is to introdu…
MoLingo: Motion-Language Alignment for Text-to-Human Motion Generation
Yannan He, Garvita Tiwari, Xiaohan Zhang +4
We introduce MoLingo, a text-to-motion (T2M) model that generates realistic, lifelike human motion by denoising in a continuous latent space. Recent works perform latent space diff…
Unimotion: Unifying 3D Human Motion Synthesis and Understanding
Chuqiao Li, Julian Chibane, Yannan He +3
We introduce Unimotion, the first unified multi-task human motion model capable of both flexible motion control and frame-level motion understanding. While existing works control a…
NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors
Yannan He, Garvita Tiwari, Tolga Birdal +2
Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses, and remains a notorious challenge. To this end, we introduc…