A wavelet-based multi-level factorized blendshape representation with distillation achieves ultra-detailed animatable avatars at 2000X lower cost and 10X smaller size, running at 24 FPS natively on Meta Quest 3.
Automatic differentiation in pytorch,
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Hybrid QFL cuts quantum transmissions from 3TNMP to {3t + 2(T-t)}NMP over T rounds while preserving near-centralized convergence and improving depolarizing-noise resilience via decentralized aggregation and Steane-code QEC.
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.
citing papers explorer
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MUA: Mobile Ultra-detailed Animatable Avatars
A wavelet-based multi-level factorized blendshape representation with distillation achieves ultra-detailed animatable avatars at 2000X lower cost and 10X smaller size, running at 24 FPS natively on Meta Quest 3.
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Practical Quantum Federated Learning for Privacy-Sensitive Healthcare: Communication Efficiency and Noise Resilience
Hybrid QFL cuts quantum transmissions from 3TNMP to {3t + 2(T-t)}NMP over T rounds while preserving near-centralized convergence and improving depolarizing-noise resilience via decentralized aggregation and Steane-code QEC.
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Equivariance2Inverse: A Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.