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Tree-VQ is presented, a post-hoc conversion of a pretrained flat VQ tokenizer into a fine-grained, arbitrary-prefix progressive representation while preserving its encoder assignments and every learned leaf vector.
Spatial audio is a key component of immersive $360^\circ$ media, yet high-quality spatial capture remains limited in real-world speech-dominant scenes. We study visually guided First-Order Ambisonics (FOA) speech spatialization in the wild: given aligned $360^\circ$ video and an omnidirectional audio track, we recover...
Tree-VQ is proposed, a progressive tree-structured vector quantization framework for learned image compression that codes progressive continuation decisions and routed branch refinements using only causally available decoded contexts and achieves a superior performance--efficiency trade-off.
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