1 citations · 2 across the 3 of their papers we have counts for
4 papers
ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models
Yijin Wei, Yawen Zou, Wenqi Cai +5
Dataset distillation (DD) aims to retain the training utility of a large dataset in a much smaller set of examples. For skeleton-based action recognition, this requires capturing d…
VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment
Zi Wang, Katsuya Hotta, Yawen Zou +4
Few-shot cross-category 3D anomaly detection aims to determine whether an unknown point cloud belongs to a target normal category using only a few normal references. Existing train…
Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior
Fangfu Liu, Diankun Wu, Yi Wei +2
Recently, 3D content creation from text prompts has demonstrated remarkable progress by utilizing 2D and 3D diffusion models. While 3D diffusion models ensure great multi-view cons…
Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset
Jianjian Qin, Chao Zhang, Chunzhi Gu +5
We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain. It is motivated by the fact that 2D-only representations in the AD task ma…