ABSTRACT
In this paper, we propose a probabilistic regression diffusion model for head pose estimation, dubbed HeadDiff, which typically addresses the rotation uncertainty, especially when faces are captured in wild conditions. Unlike conventional image-to-pose methods which cannot explicitly establish the rotational manifold of head poses, our HeadDiff aims to ensure the pose rotation via the diffusion process and in parallel, refine the mapping process iteratively. Specifically, we initially formulate the head pose estimation problem as a reverse diffusion process, defining a paradigm for progressive denoising on the manifold, which explores the uncertainty by decomposing the large gap into intermediate steps. Moreover, our HeadDiff is equipped with an isotropic Gaussian distribution by encoding the incoherence information in our rotation representation. Finally, we learn the facial relationship of nearest neighbors with a cycle-consistent constraint for robust pose estimation versus diverse shape variations. Experimental results on multiple datasets demonstrate that our proposed method outperforms existing state-of-the-art techniques without auxiliary data.
ABSTRACT
The final stadium larva of Euphaea superba Kimmins, 1936 is described and illustrated based on a male and a female specimens collected in Zhejiang province, China. The larvae were associated with the adults by mtCOI gene sequence. The larva of E. superba is diagnosed from other described members of the genus by the gena having 2-4 spines on outer side, the movable hook about 1.6 time as long as median cleft and female with primary genitalia extending to the posterior margin of abdominal segment 10.
