1 citations · 3 across the 11 of their papers we have counts for
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MAVIN: Multi-Shot Audio-Visual Generation with Customized Narrative Control
Kaiqi Liu, Yunyao Mao, Ziqi Cai +8
While recent generative models produce high-fidelity videos, they struggle with the complex narrative control required for coherent multi-shot audio-visual generation. Existing met…
UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation
Yiyan Xu, Qiulin Wang, Wenjie Wang +5
Multi-reference image generation aims to synthesize images from textual instructions while faithfully preserving subject identities from multiple reference images. Existing VLM-enh…
MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning
Xiaoyang Liu, Yunyao Mao, Wengang Zhou +1
We introduce MotionRL, the first approach to utilize Multi-Reward Reinforcement Learning (RL) for optimizing text-to-motion generation tasks and aligning them with human preference…
MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition
Weichao Zhao, Hezhen Hu, Wengang Zhou +3
Sign language recognition (SLR) has long been plagued by insufficient model representation capabilities. Although current pre-training approaches have alleviated this dilemma to so…
Learning Generalizable Human Motion Generator with Reinforcement Learning
Yunyao Mao, Xiaoyang Liu, Wengang Zhou +2
Text-driven human motion generation, as one of the vital tasks in computer-aided content creation, has recently attracted increasing attention. While pioneering research has largel…
IMD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation
Yunyao Mao, Jiajun Deng, Wengang Zhou +3
Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the ri…
Masked Motion Predictors are Strong 3D Action Representation Learners
Yunyao Mao, Jiajun Deng, Wengang Zhou +3
In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, resear…
Detect Any Shadow: Segment Anything for Video Shadow Detection
Yonghui Wang, Wengang Zhou, Yunyao Mao +1
Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to consider shadows as background and therefore does not…
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
Yunyao Mao, Wengang Zhou, Zhenbo Lu +2
In 3D action recognition, there exists rich complementary information between skeleton modalities. Nevertheless, how to model and utilize this information remains a challenging pro…
Joint Inductive and Transductive Learning for Video Object Segmentation
Yunyao Mao, Ning Wang, Wengang Zhou +1
Semi-supervised video object segmentation is a task of segmenting the target object in a video sequence given only a mask annotation in the first frame. The limited information ava…