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.
Learning transferable visual models from natural language supervision
4 Pith papers cite this work. Polarity classification is still indexing.
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CBA framework with causal intervention warm-up and prototype-guided uncertainty refinement improves unsupervised video-based visible-infrared person re-identification over extended image-based methods on HITSZ-VCM and BUPTCampus benchmarks.
A continual few-shot adaptation method combining binary cross-entropy and supervised contrastive losses with replay achieves a good trade-off between fast adaptation to unseen synthetic fingerprint styles and retention of known styles.
GCDance is a text-and-music-conditioned diffusion framework that generates genre-consistent 3D dance sequences and reports better results than prior methods on FineDance and AIST++.
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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Causal Bootstrapped Alignment for Unsupervised Video-Based Visible-Infrared Person Re-Identification
CBA framework with causal intervention warm-up and prototype-guided uncertainty refinement improves unsupervised video-based visible-infrared person re-identification over extended image-based methods on HITSZ-VCM and BUPTCampus benchmarks.
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Continual Few-shot Adaptation for Synthetic Fingerprint Detection
A continual few-shot adaptation method combining binary cross-entropy and supervised contrastive losses with replay achieves a good trade-off between fast adaptation to unseen synthetic fingerprint styles and retention of known styles.
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GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
GCDance is a text-and-music-conditioned diffusion framework that generates genre-consistent 3D dance sequences and reports better results than prior methods on FineDance and AIST++.