A flow-matching 1D diffusion transformer predicts angular radio maps from building geometry, achieving distributional fidelity, per-bin accuracy, beam selection, and Bayesian localization from a single zero-shot model across 99 environments.
Classifier-free diffusion guidance
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Flow-ERD achieves state-of-the-art realism and diversity on the WOSAC benchmark by coupling agent-type-aware flow matching with entropy-regularized distillation that prevents mode collapse during closed-loop fine-tuning.
PoDAR disentangles audio signal power from semantic content in latents using power augmentation and consistency objectives, yielding 2x faster convergence and gains of 0.055 speaker similarity and 0.22 UTMOS when applied to Stable Audio VAE with F5-TTS.
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RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
A flow-matching 1D diffusion transformer predicts angular radio maps from building geometry, achieving distributional fidelity, per-bin accuracy, beam selection, and Bayesian localization from a single zero-shot model across 99 environments.
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Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation
Flow-ERD achieves state-of-the-art realism and diversity on the WOSAC benchmark by coupling agent-type-aware flow matching with entropy-regularized distillation that prevents mode collapse during closed-loop fine-tuning.
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PoDAR: Power-Disentangled Audio Representation for Generative Modeling
PoDAR disentangles audio signal power from semantic content in latents using power augmentation and consistency objectives, yielding 2x faster convergence and gains of 0.055 speaker similarity and 0.22 UTMOS when applied to Stable Audio VAE with F5-TTS.