DrawMotion is a diffusion-based framework that fuses text and hand-drawn stickman conditions via a Multi-Condition Module and training-free guidance to generate 3D human motions.
Reducing the dimensionality of data with neural networks
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Light-ResKAN reaches 99.09% accuracy on MSTAR SAR images with 82.9 times fewer FLOPs and 163.78 times fewer parameters than VGG16 by combining KAN convolutions, Gram polynomials, and channel-wise parameter sharing.
GMAE learns disentangled view-specific and view-common embeddings via dual-path autoencoders and cross-view adversarial training to boost performance on complete and incomplete multi-view clustering tasks.
citing papers explorer
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DrawMotion: Generating 3D Human Motions by Freehand Drawing
DrawMotion is a diffusion-based framework that fuses text and hand-drawn stickman conditions via a Multi-Condition Module and training-free guidance to generate 3D human motions.
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Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition
Light-ResKAN reaches 99.09% accuracy on MSTAR SAR images with 82.9 times fewer FLOPs and 163.78 times fewer parameters than VGG16 by combining KAN convolutions, Gram polynomials, and channel-wise parameter sharing.
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Learning Disentangled Representations for Generalized Multi-view Clustering
GMAE learns disentangled view-specific and view-common embeddings via dual-path autoencoders and cross-view adversarial training to boost performance on complete and incomplete multi-view clustering tasks.