12 citations · 41 across the 15 of their papers we have counts for
15 papers
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model
Team Seedance, Heyi Chen, Siyan Chen +194
Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically f…
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Yu Gao, Haoyuan Guo, Tuyen Hoang +41
Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously b…
CLIP-AE: CLIP-assisted Cross-view Audio-Visual Enhancement for Unsupervised Temporal Action Localization
Rui Xia, Dan Jiang, Quan Zhang +2
Temporal Action Localization (TAL) has garnered significant attention in information retrieval. Existing supervised or weakly supervised methods heavily rely on labeled temporal bo…
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Ye Bai, Jingping Chen, Jitong Chen +52
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific con…
Audio Prompt Tuning for Universal Sound Separation
Yuzhuo Liu, Xubo Liu, Yan Zhao +4
Universal sound separation (USS) is a task to separate arbitrary sounds from an audio mixture. Existing USS systems are capable of separating arbitrary sources, given a few example…