Yufei Zhao

Chang Li, Jiao Guo, Yufei Zhao et al.

This paper is the first to propose an end-to-end framework of mutually reinforcing images to 3D surface recurrent neural network-like for model-adaptation indoor 3D reconstruction,where multi-view dense matching and point cloud surface optimization are mutually reinforced by a RNN-like structure rather than being treated as a separate issue.The characteristics are as follows:In the multi-view dense matching module, the model-adaptation strategy is used to fine-tune and optimize a Transformer-based multi-view dense matching DNN,so that it has the higher image feature for matching and detail expression capabilities;In the point cloud surface optimization module,the 3D surface reconstruction network based on 3D implicit field is optimized by using model-adaptation strategy,which solves the problem of point cloud surface optimization without knowing normal vector of 3D surface.To improve and finely reconstruct 3D surfaces from point cloud,smooth loss is proposed and added to this module;The MRIo3DS-Net is a RNN-like framework,which utilizes the finely optimized 3D surface obtained by PCSOM to recursively reinforce the differentiable warping for optimizing MVDMM.This refinement leads to achieving better dense matching results, and better dense matching results leads to achieving better 3D surface results recursively and mutually.Hence, model-adaptation strategy can better collaborate the differences between the two network modules,so that they complement each other to achieve the better effect;To accelerate the transfer learning and training convergence from source domain to target domain,a multi-task loss function based on Bayesian uncertainty is used to adaptively adjust the weights between the two networks loss functions of MVDMM and PCSOM;In this multi-task cascade network framework,any modules can be replaced by any state-of-the-art networks to achieve better 3D reconstruction results.

Ming Meng, Yufei Zhao, Bo Zhang et al.

Talking head synthesis, an advanced method for generating portrait videos from a still image driven by specific content, has garnered widespread attention in virtual reality, augmented reality and game production. Recently, significant breakthroughs have been made with the introduction of novel models such as the transformer and the diffusion model. Current methods can not only generate new content but also edit the generated material. This survey systematically reviews the technology, categorizing it into three pivotal domains: portrait generation, driven mechanisms, and editing techniques. We summarize milestone studies and critically analyze their innovations and shortcomings within each domain. Additionally, we organize an extensive collection of datasets and provide a thorough performance analysis of current methodologies based on various evaluation metrics, aiming to furnish a clear framework and robust data support for future research. Finally, we explore application scenarios of talking head synthesis, illustrate them with specific cases, and examine potential future directions.