7 accepted papers
Image composition has advanced significantly with large-scale pre-trained T2I diffusion models. Despite progress in same-domain composition, cross-domain composition remains under-explored. The main challenges are the stochastic nature of diffusion models and the style gap between input images, lead…
Training a generic 3D face reconstruction model in a self-supervised manner using large-scale, in-the-wild 2D face image datasets enhances robustness to varying lighting conditions and occlusions while allowing the model to capture animatable wrinkle details across diverse facial expressions. Howeve…
Text-based person search aims to retrieve the specified person images given a textual description. The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including…
While impressive progress has recently been made in image-oriented facial attribute translation, shape-oriented 3D facial attribute translation remains an unsolved issue. This is primarily limited by the lack of 3D generative models and ineffective usage of 3D facial data. We propose a learning fram…
3D facial landmark detection is extensively used in many research fields such as face registration, facial shape analysis, and face recognition. Most existing methods involve traditional features and 3D face models for the detection of landmarks, and their performances are limited by the hand-crafte…
Dense 3D face correspondence is a fundamental and challenging issue in the literature of 3D face analysis. Correspondence between two 3D faces can be viewed as a non-rigid registration problem that one deforms into the other, which is commonly guided by a few facial landmarks in many existing works.…
Many previous literatures use landmarks to guide the cor- respondence of 3D faces. However, these landmarks, either manually or automatically annotated, are hard to define consistently across differ- ent faces in many circumstances. We propose a general framework for dense correspondence of 3D faces…