17 papers
OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
Hongyu Liu, Chun Wang, Feng Gao +6
We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce…
Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning
Chen Zhao, Jiajun Ma, Qilong Huang +6
While Multimodal Large Language Models (MLLMs) have advanced video understanding, achieving precise temporal and cross-modal alignment in audiovisual video captioning remains a for…
LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts
Chen Zhao, Jiawei Chen, Hongyu Li +6
Recent advances in video diffusion models have significantly improved visual quality, yet ultra-high-resolution (UHR) video generation remains a formidable challenge due to the com…
TurboTalk: Progressive Distillation for One-Step Audio-Driven Talking Avatar Generation
Xiangyu Liu, Feng Gao, Xiaomei Zhang +4
Existing audio-driven video digital human generation models rely on multi-step denoising, resulting in substantial computational overhead that severely limits their deployment in r…
From Competition to Coopetition: Coopetitive Training-Free Image Editing Based on Text Guidance
Jinhao Shen, Haoqian Du, Xulu Zhang +2
Text-guided image editing, a pivotal task in modern multimedia content creation, has seen remarkable progress with training-free methods that eliminate the need for additional opti…
ECHO: Towards Emotionally Appropriate and Contextually Aware Interactive Head Generation
Xiangyu Kong, Xiaoyu Jin, Yihan Pan +6
In natural face-to-face interaction, participants seamlessly alternate between speaking and listening, producing facial behaviors (FBs) that are finely informed by long-range conte…