36 citations · 40 across the 6 of their papers we have counts for
6 papers
Auto DragGAN: Editing the Generative Image Manifold in an Autoregressive Manner
Pengxiang Cai, Zhiwei Liu, Guibo Zhu +2
Pixel-level fine-grained image editing remains an open challenge. Previous works fail to achieve an ideal trade-off between control granularity and inference speed. They either fai…
PFDM: Parser-Free Virtual Try-on via Diffusion Model
Yunfang Niu, Dong Yi, Lingxiang Wu +3
Virtual try-on can significantly improve the garment shopping experiences in both online and in-store scenarios, attracting broad interest in computer vision. However, to achieve h…
What Decreases Editing Capability? Domain-Specific Hybrid Refinement for Improved GAN Inversion
Pu Cao, Lu Yang, Dongxv Liu +3
Recently, inversion methods have focused on additional high-rate information in the generator (e.g., weights or intermediate features) to refine inversion and editing results from…
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation
Zhiwei Liu, Xiangyu Zhu, Lu Yang +7
3D human pose and shape recovery from a monocular RGB image is a challenging task. Existing learning based methods highly depend on weak supervision signals, e.g. 2D and 3D joint l…
Identity-Guided Human Semantic Parsing for Person Re-Identification
Kuan Zhu, Haiyun Guo, Zhiwei Liu +2
Existing alignment-based methods have to employ the pretrained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpac…
Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection
Zhiwei Liu, Xiangyu Zhu, Guosheng Hu +5
Recently, deep learning based facial landmark detection has achieved great success. Despite this, we notice that the semantic ambiguity greatly degrades the detection performance.…