66 citations · 90 across the 2 of their papers we have counts for
6 papers · 1 filter
MoVT: Video-Augmented Motion Tokenizer for Text-to-Motion Generation
Beibei Jing, Tianle Guo, Youjia Zhang +5
Text-driven 3D human motion generation models face significant challenges in responding to diverse and unconstrained textual prompts, primarily due to the limited availability of 3…
C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction
Luoyuan Xu, Tao Guan, Yuesong Wang +4
There is an emerging effort to combine the two popular 3D frameworks using Multi-View Stereo (MVS) and Neural Implicit Surfaces (NIS) with a specific focus on the few-shot / sparse…
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
Yawei Luo, Ping Liu, Tao Guan +2
We aim at the problem named One-Shot Unsupervised Domain Adaptation. Unlike traditional Unsupervised Domain Adaptation, it assumes that only one unlabeled target sample can be avai…
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
Yawei Luo, Ping Liu, Tao Guan +2
For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image class…
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
Yawei Luo, Liang Zheng, Tao Guan +2
We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distrib…
Macro-Micro Adversarial Network for Human Parsing
Yawei Luo, Zhedong Zheng, Liang Zheng +3
In human parsing, the pixel-wise classification loss has drawbacks in its low-level local inconsistency and high-level semantic inconsistency. The introduction of the adversarial n…