activity
20242026
collaborators

7 papers

cs.MM2026

Encore: Infinite Audio-Video Generation with Adaptive Signal Routing

Shaohua Pan, Junbao Chen, Shengyi He +8

Existing audio-video generation methods produce well-synchronized clips but are limited to short durations, while long-video generation methods extend duration through chunk-based…

cs.CV2026

Towards Error-Free Long Video Generation

Shuning Chang, Weihua Chen, Jiasheng Tang +8

Recent advances in video generation have made minute-level synthesis possible; however, generating long videos remains challenging due to error accumulation, attribute drift, and t…

cs.CV2026

Temporal-Aware Reasoning Optimization for Video Temporal Grounding

Minghang Zheng, Zihao Yin, Yi Yang +2

Multi-modal Large Language Models (MLLMs) have achieved remarkable progress in video temporal grounding with reinforcement learning for generating reasoning paths. However, existin…

cs.CV2026

FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion

Yu Lu, Junjie Yang, Piotr Koniusz +2

Autoregressive video generators synthesize long videos by generating successive temporal segments, but their historical KV cache grows with video length. Existing bounded-cache met…

cs.CV2026

UniTalking: A Unified Audio-Video Framework for Talking Portrait Generation

Hebeizi Li, Zihao Liang, Benyuan Sun +4

While state-of-the-art audio-video generation models like Veo3 and Sora2 demonstrate remarkable capabilities, their closed-source nature makes their architectures and training para…

cs.CV2025

Lumos-1: On Autoregressive Video Generation with Discrete Diffusion from a Unified Model Perspective

Hangjie Yuan, Weihua Chen, Jun Cen +11

Autoregressive large language models (LLMs) have unified a vast range of language tasks, inspiring preliminary efforts in autoregressive (AR) video generation. Existing AR video ge…

cs.CV2024

SparseDiT: Token Sparsification for Efficient Diffusion Transformer

Shuning Chang, Pichao Wang, Jiasheng Tang +2

Diffusion Transformers (DiT) are renowned for their impressive generative performance; however, they are significantly constrained by considerable computational costs due to the qu…