19 citations · 30 across the 9 of their papers we have counts for
9 papers · 1 filter
Video Object Segmentation-Aware Audio Generation
Ilpo Viertola, Vladimir Iashin, Esa Rahtu
Existing multimodal audio generation models often lack precise user control, which limits their applicability in professional Foley workflows. In particular, these models focus on…
Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder
Vladimir Iashin, Horace Lee, Dan Schofield +1
Camera traps are revolutionising wildlife monitoring by capturing vast amounts of visual data; however, the manual identification of individual animals remains a significant bottle…
Temporally Aligned Audio for Video with Autoregression
Ilpo Viertola, Vladimir Iashin, Esa Rahtu
We introduce V-AURA, the first autoregressive model to achieve high temporal alignment and relevance in video-to-audio generation. V-AURA uses a high-framerate visual feature extra…
Synchformer: Efficient Synchronization from Sparse Cues
Vladimir Iashin, Weidi Xie, Esa Rahtu +1
Our objective is audio-visual synchronization with a focus on 'in-the-wild' videos, such as those on YouTube, where synchronization cues can be sparse. Our contributions include a…
Sparse in Space and Time: Audio-visual Synchronisation with Trainable Selectors
Vladimir Iashin, Weidi Xie, Esa Rahtu +1
The objective of this paper is audio-visual synchronisation of general videos 'in the wild'. For such videos, the events that may be harnessed for synchronisation cues may be spati…
Taming Visually Guided Sound Generation
Vladimir Iashin, Esa Rahtu
Recent advances in visually-induced audio generation are based on sampling short, low-fidelity, and one-class sounds. Moreover, sampling 1 second of audio from the state-of-the-art…