arXiv Computer Vision
Oct 12

AV-FlowEdit: Training-Free Prompt-Driven Audio-Visual Editing with Event-Aware Spectral Trajectory Preservation

AV-FlowEdit is a training‑free, inversion‑free framework that enables prompt‑driven audio‑visual editing using joint audio‑video diffusion models. It leverages source‑to‑target flow differences to guide edits while preserving non‑target structure and cross‑modal correspondence. The method includes a Cross‑Modal Event‑Aware Spectral Trajectory Preservation module that suppresses drift by combining flow saliency, aligned audio activity, multiscale low‑frequency displacement, and diffusion‑stage scheduling, achieving strong prompt alignment, temporal consistency, and competitive audio quality.

By Shaohua Pan, Xiao Liu
arXiv Computer Vision
Sep 23

Vorch-Human: Unified Multi-Task Human-Centric Generation via Long-Horizon Continuation

Vorch-Human is a unified framework for human‑centric audio‑visual generation that handles multiple tasks—animating a person from speech, jointly generating speech and video from a voice reference, and synthesizing a scene from paired appearance and voice references—using a single dual‑stream audio‑video diffusion transformer. The model incorporates clean condition‑audio and condition‑video tokens, per‑token task embeddings, temporal position types, condition masks, and a shared multimodal prompt encoder to express diverse inputs such as driving speech, timbre examples, first frames, and subject images. A two‑level data pipeline supplies the necessary supervision by extracting speech, appearance, and timbre annotations from clips and linking consistent identity and outfit references across videos, while a frozen‑prefix recurrence enables long‑form audio‑driven generation with reduced boundary discontinuity and identity drift.

By Yang Ding, Haoran Yu, Xin Ma, Yulei Lu, Menglin Han, Yaole Wang, Siqian Yang, Gang Yue, Kaihao Zhang, Yaohui Wang, Lin Ma
arXiv Computer Vision
Sep 7

AVENUE: Audio-Video EditiNg Understanding and Evaluation

AVENUE is a new benchmark and evaluation framework for audio‑video editing that includes 1,291 source clips and 7,957 editing instructions covering audio‑targeted, video‑targeted, and coupled edits. It introduces a sample‑specific, modality‑aware evaluation that specifies the intended change and the content that must remain unchanged. The study applies this framework to joint, sequential, and separate editing models, revealing that existing models often alter unintended modalities, highlighting a key challenge in controllable AV editing.

By Hayeon Kim, Yoojin Jang, Jaejun Yoo