1 citations · 2 across the 11 of their papers we have counts for
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ULIFLOW: Unified Latent Human-Object Interaction Flow
Wenqi Cai, Chunzhi Gu, Weichen Zhou +5
Text-conditioned 3D human--object interaction (HOI) generation remains challenging because individually plausible human and object motions do not necessarily form a coherent intera…
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…
Dataset Distillation Based on Saliency-Driven Prototype Alignment
Yawen Zou, Wenqi Cai, Guang Li +3
Dataset distillation aims to synthesize compact datasets that can approximate the performance of full-data training while significantly reducing computational and storage costs. Ho…
Learning Emotion from Motion: Kinetic Multi-Stream Skeleton Modeling with Metadata-Conditioned Weak Label Distributions
Sosuke Suzuki, Yijin Wei, Koichiro Kamide +3
Skeleton-based emotion recognition from body motion remains challenging because emotional expressions are often characterized by subtle dynamic and relational motion cues, and hard…
Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention
Weichen Zhou, Yawen Zou, Chunzhi Gu +3
We introduce a controlled subspace intervention framework to investigate how self-supervised Vision Transformers (ViTs) encode dense geometric information. While linear probing is…
EVLF: Early Vision-Language Fusion for Generative Dataset Distillation
Wenqi Cai, Yawen Zou, Guang Li +2
Dataset distillation (DD) aims to synthesize compact training sets that enable models to achieve high accuracy with significantly fewer samples. Recent diffusion-based DD methods c…
Does Semantic Noise Initialization Transfer from Images to Videos? A Paired Diagnostic Study
Yixiao Jing, Chaoyu Zhang, Zixuan Zhong +1
Semantic noise initialization has been reported to improve robustness and controllability in image diffusion models. Whether these gains transfer to text-to-video (T2V) generation…
3D Human-Human Interaction Anomaly Detection
Shun Maeda, Chunzhi Gu, Koichiro Kamide +3
Human-centric anomaly detection (AD) has been primarily studied to specify anomalous behaviors in a single person. However, as humans by nature tend to act in a collaborative manne…
Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
Koichiro Kamide, Shunsuke Sakai, Shun Maeda +2
Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training. Existing methods typically follow a one-model-per-category p…
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering
Zi Wang, Katsuya Hotta, Koichiro Kamide +3
High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significa…
Label-Consistent Dataset Distillation with Detector-Guided Refinement
Yawen Zou, Guang Li, Zi Wang +2
Dataset distillation (DD) aims to generate a compact yet informative dataset that achieves performance comparable to the original dataset, thereby reducing demands on storage and c…
Dataset Distillation via Vision-Language Category Prototype
Yawen Zou, Guang Li, Duo Su +3
Dataset distillation (DD) condenses large datasets into compact yet informative substitutes, preserving performance comparable to the original dataset while reducing storage, trans…
Incremental Pseudo-Labeling for Black-Box Unsupervised Domain Adaptation
Yawen Zou, Chunzhi Gu, Jun Yu +2
Black-Box unsupervised domain adaptation (BBUDA) learns knowledge only with the prediction of target data from the source model without access to the source data and source model,…
Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows
Shun Maeda, Chunzhi Gu, Jun Yu +3
We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of…
Orientation-Aware Leg Movement Learning for Action-Driven Human Motion Prediction
Chunzhi Gu, Chao Zhang, Shigeru Kuriyama
The task of action-driven human motion prediction aims to forecast future human motion based on the observed sequence while respecting the given action label. It requires modeling…