5 papers
Fast Inference of Visual Autoregressive Model with Adjacency-Adaptive Dynamical Draft Trees
Haodong Lei, Hongsong Wang, Xin Geng +2
Autoregressive (AR) image models achieve diffusion-level quality but suffer from sequential inference, requiring approximately 2,000 steps for a 576x576 image. Speculative decoding…
ReAlign: Text-to-Motion Generation via Step-Aware Reward-Guided Alignment
Wanjiang Weng, Xiaofeng Tan, Junbo Wang +3
Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based me…
IBNorm: Information-Bottleneck Inspired Normalization for Representation Learning
Xiandong Zou, Jia Li, Xiaotong Yuan +1
Normalization is fundamental to deep learning, but existing approaches such as BatchNorm, LayerNorm, and RMSNorm are variance-centric by enforcing zero mean and unit variance, stab…
ReAlign: Bilingual Text-to-Motion Generation via Step-Aware Reward-Guided Alignment
Wanjiang Weng, Xiaofeng Tan, Hongsong Wang +1
Bilingual text-to-motion generation, which synthesizes 3D human motions from bilingual text inputs, holds immense potential for cross-linguistic applications in gaming, film, and r…
SoPo: Text-to-Motion Generation Using Semi-Online Preference Optimization
Xiaofeng Tan, Hongsong Wang, Xin Geng +1
Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fi…