Won the DataHack challenge by orchestrating a spatial-temporal action recognition pipeline utiliz...Won the DataHack challenge by orchestrating a spatial-temporal action recognition pipeline utiliz...
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Won the DataHack challenge by orchestrating a spatial-temporal action recognition pipeline utilizing OpenPose and a Multi-Domain Regularized Graph Convolutional Network (MDR-GCN) to analyze skeletal sequence chunks, accurately extracting and time-stamping complex figure skating choreography into structured JSON data.
Developed an automated multi-modal mapping algorithm that dynamically translated kinematic predictions into contextual, segmented text prompts, successfully matching specific physical movements (e.g., jumps, spins) with targeted musical orchestration parameters.
Integrated the InspireMusic generative AI stack utilizing text-to-music and temporal continuation modes, chaining dynamic segments to synthesize seamless, multi-minute 48 kHz stereo audio tracks perfectly synchronized with the input video.
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