12 citations · 24 across the 8 of their papers we have counts for
8 papers
SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance
Guibao Shen, Luozhou Wang, Jiantao Lin +9
Recent advancements in text-to-image generation have been propelled by the development of diffusion models and multi-modality learning. However, since text is typically represented…
DVIS++: Improved Decoupled Framework for Universal Video Segmentation
Tao Zhang, Xingye Tian, Yikang Zhou +7
We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video…
Stable Segment Anything Model
Qi Fan, Xin Tao, Lei Ke +6
The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts which, however, often require good skills to specify. To make SAM robust to…
Temporal-Aware Refinement for Video-based Human Pose and Shape Recovery
Ming Chen, Yan Zhou, Weihua Jian +2
Though significant progress in human pose and shape recovery from monocular RGB images has been made in recent years, obtaining 3D human motion with high accuracy and temporal cons…
Towards Practical Capture of High-Fidelity Relightable Avatars
Haotian Yang, Mingwu Zheng, Wanquan Feng +5
In this paper, we propose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared to previous methods, T…
Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis
Chunyu Qiang, Peng Yang, Hao Che +3
Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker's timbre. In most previous methods, the…
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing
Wanfeng Zheng, Qiang Li, Xiaoyan Guo +2
Text-driven image manipulation is developed since the vision-language model (CLIP) has been proposed. Previous work has adopted CLIP to design a text-image consistency-based object…
ITTR: Unpaired Image-to-Image Translation with Transformers
Wanfeng Zheng, Qiang Li, Guoxin Zhang +2
Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data. By utilizing CNN in extracting local semantics, v…