Quenching depends on morphologies: implications from the ultraviolet-optical radial color distributions in Green Valley Galaxies

In this Letter, we analyse the radial UV-optical color distributions in a sample of low redshift green valley (GV) galaxies, with the Galaxy Evolution Explorer (GALEX)+Sloan Digital Sky Survey (SDSS) images, to investigate how the residual recent star formation distribute in these galaxies. We find that the dust-corrected $u-r$ colors of early-type galaxies (ETGs) are flat out to $R_{90}$, while the colors turn blue monotonously when $r>0.5R_{50}$ for late-type galaxies (LTGs). More than a half of the ETGs are blue-cored and have remarkable positive NUV$-r$ color gradients, suggesting that their star formation are centrally concentrated; the rest have flat color distributions out to $R_{90}$. The centrally concentrated star formation activity in a large portion of ETGs is confirmed by the SDSS spectroscopy, showing that $\sim$50 % ETGs have EW(H$\rm \alpha$)$>6.0$ \AA. For the LTGs, 95% of them show uniform radial color profiles, which can be interpreted as a red bulge plus an extended blue disk. The links between the two kinds of ETGs, e.g., those objects having remarkable "blue-cored" and those having flat color gradients, are less known and require future investigations. It is suggested that the LTGs follow a general picture that quenching first occur in the core regions, and then finally extend to the rest of the galaxy. Our results can be re-examined and have important implications for the IFU surveys, such as MaNGA and SAMI.Comment: ApJ Letter, accepted. Five figure

In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations

Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations to such in-the-wild corpora is hardly feasible in practice. Most existing 3D labelled data sets are either synthetically created or feature in-studio images. 3D pose estimation algorithms trained on such data often have limited ability to generalize to real world scene diversity. We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. Our algorithm can be jointly trained on image data with 3D labels and image data with only 2D labels. It achieves state-of-the-art accuracy on challenging in-the-wild data.Comment: Accepted to CVPR 201

LiveCap: Real-time Human Performance Capture from Monocular Video

We present the first real-time human performance capture approach that reconstructs dense, space-time coherent deforming geometry of entire humans in general everyday clothing from just a single RGB video. We propose a novel two-stage analysis-by-synthesis optimization whose formulation and implementation are designed for high performance. In the first stage, a skinned template model is jointly fitted to background subtracted input video, 2D and 3D skeleton joint positions found using a deep neural network, and a set of sparse facial landmark detections. In the second stage, dense non-rigid 3D deformations of skin and even loose apparel are captured based on a novel real-time capable algorithm for non-rigid tracking using dense photometric and silhouette constraints. Our novel energy formulation leverages automatically identified material regions on the template to model the differing non-rigid deformation behavior of skin and apparel. The two resulting non-linear optimization problems per-frame are solved with specially-tailored data-parallel Gauss-Newton solvers. In order to achieve real-time performance of over 25Hz, we design a pipelined parallel architecture using the CPU and two commodity GPUs. Our method is the first real-time monocular approach for full-body performance capture. Our method yields comparable accuracy with off-line performance capture techniques, while being orders of magnitude faster

Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation

We propose a novel approach to jointly perform 3D shape retrieval and pose estimation from monocular images.In order to make the method robust to real-world image variations, e.g. complex textures and backgrounds, we learn an embedding space from 3D data that only includes the relevant information, namely the shape and pose. Our approach explicitly disentangles a shape vector and a pose vector, which alleviates both pose bias for 3D shape retrieval and categorical bias for pose estimation. We then train a CNN to map the images to this embedding space, and then retrieve the closest 3D shape from the database and estimate the 6D pose of the object. Our method achieves 10.3 median error for pose estimation and 0.592 top-1-accuracy for category agnostic 3D object retrieval on the Pascal3D+ dataset, outperforming the previous state-of-the-art methods on both tasks

From outside-in to inside-out: galaxy assembly mode depends on stellar mass

In this Letter, we investigate how galaxy mass assembly mode depends on stellar mass $M_{\ast}$, using a large sample of $\sim$10, 000 low redshift galaxies. Our galaxy sample is selected to have SDSS R_{90}>5\arcsec.0, which allows the measures of both the integrated and the central NUV$-r$ color indices. We find that: in the $M_{\ast}-($ NUV$-r$) green valley, the M_{\ast}<10^{10}~M_{\sun} galaxies mostly have positive or flat color gradients, while most of the M_{\ast}>10^{10.5}~M_{\sun} galaxies have negative color gradients. When their central $D_{n}4000$ index values exceed 1.6, the M_{\ast}<10^{10.0}~M_{\sun} galaxies have moved to the UV red sequence, whereas a large fraction of the M_{\ast}>10^{10.5}~M_{\sun} galaxies still lie on the UV blue cloud or the green valley region. We conclude that the main galaxy assembly mode is transiting from "the outside-in" mode to "the inside-out" mode at M_{\ast} 10^{10.5}~M_{\sun}. We argue that the physical origin of this is the compromise between the internal and the external process that driving the star formation quenching in galaxies. These results can be checked with the upcoming large data produced by the on-going IFS survey projects, such as CALIFA, MaNGA and SAMI in the near future.Comment: Accepted for publication in ApJL,6 pages, 5 figure

Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB

We propose a new single-shot method for multi-person 3D pose estimation in general scenes from a monocular RGB camera. Our approach uses novel occlusion-robust pose-maps (ORPM) which enable full body pose inference even under strong partial occlusions by other people and objects in the scene. ORPM outputs a fixed number of maps which encode the 3D joint locations of all people in the scene. Body part associations allow us to infer 3D pose for an arbitrary number of people without explicit bounding box prediction. To train our approach we introduce MuCo-3DHP, the first large scale training data set showing real images of sophisticated multi-person interactions and occlusions. We synthesize a large corpus of multi-person images by compositing images of individual people (with ground truth from mutli-view performance capture). We evaluate our method on our new challenging 3D annotated multi-person test set MuPoTs-3D where we achieve state-of-the-art performance. To further stimulate research in multi-person 3D pose estimation, we will make our new datasets, and associated code publicly available for research purposes.Comment: International Conference on 3D Vision (3DV), 201

MonoPerfCap: Human Performance Capture from Monocular Video

We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201

Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems

Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. From a human vision perspective, there're two key factors that affect users' behaviors: items' attractiveness and their matching degree with users' interests. This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective. Telepath is a combination of a convolutional neural network (CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its CNN subnetwork simulates the human vision system to extract key visual signals of items' attractiveness and generate corresponding activations. Its RNN and DNN subnetworks simulate cerebral cortex to understand users' interest based on the activations generated from browsed items. In practice, the Telepath model has been launched to JD's recommender system and advertising system. For one of the major item recommendation blocks on the JD app, click-through rate (CTR), gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71% respectively. For several major ads publishers of JD demand-side platform, CTR, GMV and return on investment have increased 6.58%, 61.72% and 65.57% respectively by the first launch, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.Comment: 8 pages, 11 figures, 1 tabl