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20192026
most citedMIST: Multiple Instance Self-Training Framework for Video Anomaly Detection

19 citations · 35 across the 13 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2023★ 2 cited

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…

cs.CV2023

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…