For 3D hand and body pose estimation task in depth image, a novel anchor-based approach termed Anchor-to-Joint regression network (A2J) with the end-to-end learning ability is proposed. Within A2J, anchor points able to capture global-local spatial context information are densely set on depth image as local regressors for the joints. They contribute to predict the positions of the joints in ensemble way to enhance generalization ability. The proposed 3D articulated pose estimation paradigm is different from the state-of-the-art encoder-decoder based FCN, 3D CNN and point-set based manners. To discover informative anchor points towards certain joint, anchor proposal procedure is also proposed for A2J. Meanwhile 2D CNN (i.e., ResNet-50) is used as backbone network to drive A2J, without using time-consuming 3D convolutional or deconvolutional layers. The experiments on 3 hand datasets and 2 body datasets verify A2J's superiority. Meanwhile, A2J is of high running speed around 100 FPS on single NVIDIA 1080Ti GPU.

SIL040, an introgression line (IL) developed by introgressing chromosomal segments from an accession of Oryza rufipogon into an indica cultivar Guichao 2, showed significantly less grains per panicle than the recurrent parent Guichao 2. Quantitative trait locus (QTL) analysis in F2 and F3 generations derived from the cross between SIL040 and Guichao 2 revealed that gpa7, a QTL located on the short arm of chromosome 7, was responsible of this variation. Alleles from O. rufipogon decreased grains per panicle. To fine mapping of gpa7, a high-resolution map with 1,966 F2 plants derived from the cross between SIL040 and Guichao 2 using markers flanking gpa7 was constructed, and detailed quantitative evaluation of the structure of main panicle of each of F3 families derived from recombinants screened was performed. By two-step substitution mapping, gpa7 was finally narrowed down to a 35-kb region that contains five predicted genes in cultivated rice. The fact that QTLs for five panicle traits (length of panicle, primary branches per panicle, secondary branches per panicle, grains on primary branches and grains on secondary branches) were all mapped in the same interval as that for gpa7 suggested that this locus was associated with panicle structure, showing pleiotropic effects. The characterizing of panicle structure of IL SIL040 further revealed that, during the domestication from common wild allele to cultivated rice one at gpa7, not only the number of branches and grains per panicle increased significantly, more importantly, but also the ratio of secondary branches per panicle to total branches per panicle and the ratio of grains on secondary branches per panicle to total grains per panicle increased significantly. All these results reinforced the idea that gpa7 might play an important role in the regulation of grain number per panicle and the ratio of secondary branches per panicle during the domestication of rice panicle.

We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and handobject interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS'19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27mm to 13mm mean joint error. Our analyses highlight the impacts of: Data pre-processing, ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones.

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