6 papers · 1 filter
AutoRefiner: Improving Autoregressive Video Diffusion Models via Reflective Refinement Over the Stochastic Sampling Path
Zhengyang Yu, Akio Hayakawa, Masato Ishii +4
Autoregressive video diffusion models (AR-VDMs) show strong promise as scalable alternatives to bidirectional VDMs, enabling real-time and interactive applications. Yet there remai…
Schrodinger Audio-Visual Editor: Object-Level Audiovisual Removal
Weihan Xu, Kan Jen Cheng, Koichi Saito +10
Joint editing of audio and visual content is crucial for precise and controllable content creation. This new task poses challenges due to the limitations of paired audio-visual dat…
Coherent Audio-Visual Editing via Conditional Audio Generation Following Video Edits
Masato Ishii, Akio Hayakawa, Takashi Shibuya +1
We introduce a novel pipeline for joint audio-visual editing that enhances the coherence between edited video and its accompanying audio. Our approach first applies state-of-the-ar…
SoundReactor: Frame-level Online Video-to-Audio Generation
Koichi Saito, Julian Tanke, Christian Simon +7
Prevailing Video-to-Audio (V2A) generation models operate offline, assuming an entire video sequence or chunks of frames are available beforehand. This critically limits their use…
TITAN-Guide: Taming Inference-Time AligNment for Guided Text-to-Video Diffusion Models
Christian Simon, Masato Ishii, Akio Hayakawa +4
In the recent development of conditional diffusion models still require heavy supervised fine-tuning for performing control on a category of tasks. Training-free conditioning via g…
Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance
Akio Hayakawa, Masato Ishii, Takashi Shibuya +1
We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio synthesis. Inspired by traditional…