19 citations · 37 across the 14 of their papers we have counts for
15 papers
TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control
Wei-Jin Huang, Yuan-Ming Li, Kun-Yu Lin +8
Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment slow and memory-intensive. W…
PhysiGen: Integrating Collision-Aware Physical Constraints for High-Fidelity Human-Human Interaction Generation
Nan Lei, Yuan-Ming Li, Ling-An Zeng +5
Despite substantial progress in text-driven 3D human motion synthesis, generating realistic multi-person interaction sequences remains challenging. Notably, body inter-penetration…
Free-viewpoint Human Animation with Pose-correlated Reference Selection
Fa-Ting Hong, Zhan Xu, Haiyang Liu +6
Diffusion-based human animation aims to animate a human character based on a source human image as well as driving signals such as a sequence of poses. Leveraging the generative ca…
Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation
Shuling Zhao, Fa-Ting Hong, Xiaoshui Huang +1
Talking head video generation aims to generate a realistic talking head video that preserves the person's identity from a source image and the motion from a driving video. Despite…
DreamHead: Learning Spatial-Temporal Correspondence via Hierarchical Diffusion for Audio-driven Talking Head Synthesis
Fa-Ting Hong, Yunfei Liu, Yu Li +3
Audio-driven talking head synthesis strives to generate lifelike video portraits from provided audio. The diffusion model, recognized for its superior quality and robust generaliza…
Learning Online Scale Transformation for Talking Head Video Generation
Fa-Ting Hong, Dan Xu
One-shot talking head video generation uses a source image and driving video to create a synthetic video where the source person's facial movements imitate those of the driving vid…
Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generation
Fa-Ting Hong, Dan Xu
Talking head video generation aims to animate a human face in a still image with dynamic poses and expressions using motion information derived from a target-driving video, while m…
DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Fa-Ting Hong, Li Shen, Dan Xu
Predominant techniques on talking head generation largely depend on 2D information, including facial appearances and motions from input face images. Nevertheless, dense 3D facial g…
Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning
Jia-Run Du, Jia-Chang Feng, Kun-Yu Lin +5
Weakly Supervised Temporal Action Localization (WSTAL) aims to localize and classify action instances in long untrimmed videos with only video-level category labels. Due to the lac…
Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Fa-Ting Hong, Longhao Zhang, Li Shen +1
Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving vide…
Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization
Fa-Ting Hong, Jia-Chang Feng, Dan Xu +2
Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Bo…
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Jia-Chang Feng, Fa-Ting Hong, Wei-Shi Zheng
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insuff…
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
Fa-Ting Hong, Xuanteng Huang, Wei-Hong Li +1
We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but wit…
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection
Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng
Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a h…
Learning to Learn Relation for Important People Detection in Still Images
Wei-Hong Li, Fa-Ting Hong, Wei-Shi Zheng
Humans can easily recognize the importance of people in social event images, and they always focus on the most important individuals. However, learning to learn the relation betwee…