Multi-Person Pose Regression With Distribution-Aware Single-Stage Models.

Understanding human posture is a challenging topic, which encompasses several tasks, e.g., pose estimation, body mesh recovery and pose tracking. In this article, we propose a novel Distribution-Aware Single-stage (DAS) model for the pose-related tasks. The proposed DAS model estimates human position and localizes joints simultaneously, which requires only a single pass. Meanwhile, we utilize normalizing flow to enable DAS to learn the true distribution of joint locations, rather than making simple Gaussian or Laplacian assumptions. This provides a pivotal prior and greatly boosts the accuracy of regression-based methods, thus making DAS achieve comparable performance to the volumetric-based methods. We also introduce a recursively update strategy to progressively approach the regression target, reducing the difficulty of regression and improving the regression performance. We further adapt DAS to multi-person mesh recovery and pose tracking tasks and achieve considerable performance on both tasks. Comprehensive experiments on CMU Panoptic and MuPoTS-3D demonstrate the superior efficiency of DAS, specifically 1.5 times speedup over previous best method, and its state-of-the-art accuracy for multi-person pose estimation. Extensive experiments on 3DPW and PoseTrack2018 indicate the effectiveness and efficiency of DAS for human body mesh recovery and pose tracking, respectively, which prove the generality of our proposed DAS model.

Spatial-Aware Texture Transformer for High-Fidelity Garment Transfer.

Garment transfer aims to transfer the desired garment from a model image with the desired clothing to a target person, which has attracted a great deal of attention due to its wider potential applications. However, considering the model and target persons are often given at different views, body shapes and poses, realistic garment transfer is facing the following challenges that have not been well addressed: 1) deforming the garment; 2) inferring unobserved appearance; 3) preserving fine texture details. To tackle these challenges, we propose a novel SPatial-Aware Texture Transformer (SPATT) model. Different from existing models, SPATT establishes correspondence and infers unobserved clothing appearance by leveraging the spatial prior information of a UV-space. Specifically, the source image is transformed into a partial UV texture map guided by the extracted dense pose. To better infer the unseen appearance utilizing seen region, we first propose a novel coordinate-prior map that defines the spatial relationship between the coordinates in the UV texture map, and design an algorithm to compute it. Based on the proposed coordinate-prior map, we present a novel spatial-aware texture generation network to complete the partial UV texture. In the second stage, we first transform the completed UV texture to fit the target person. To polish the details and improve realism, we introduce a refinement generative network conditioned on the warped image and source input. Compared with existing frameworks as shown experimentally, the proposed framework can generate more realistic images with better-preserved texture details. Furthermore, difficult cases where two persons have large pose and view differences can also be well handled by SPATT.

Hierarchical Contextual Refinement Networks for Human Pose Estimation.

Predicting human pose in the wild is a challenging problem due to high flexibility of joints and possible occlusion. Existing approaches generally tackle the difficulties either by holistic prediction or multi-stage processing, which suffer from poor performance for locating challenging joints or high computational cost. In this paper, we propose a new Hierarchical Contextual Refinement Network (HCRN) to robustly predict human poses in an efficient manner, where human body joints of different complexities are processed at different layers in a context hierarchy. Different from existing approaches, our proposed model predicts positions of joints from easy to difficult in a single stage through effectively exploiting informative contexts provided in the previous layer. Such approach offers two appealing advantages over state-of-the-arts: (1) more accurate than predicting all the joints together and (2) more efficient than multi-stage processing methods. We design a Contextual Refinement Unit (CRU) to implement the proposed model, which enables auto-diffusion of joint detection results to effectively transfer informative context from easy joints to difficult ones. In this way, difficult joints can be reliably detected even in presence of occlusion or severe distracting factors. Multiple CRUs are organized into a tree-structured hierarchy which is end-to-end trainable and does not require processing joints for multiple iterations. Comprehensive experiments evaluate the efficacy and efficiency of the proposed HCRN model to improve well-established baselines and achieve new state-of-the-art on multiple human pose estimation benchmarks.

[The study of tetrandrine on reversion of P170 and apoptosis of obtained multi-drug resistance of mice S180's tumour cell].

OBJECTIVE: To observe the effect of tetrandrine on reversion of mice S180's obtained multi-drug resistance tumor cell induced by chemotherapy by PFC. And then discuss the molecular mechanism of it for the use of TCM in clinic to restrain the drug-resistant of chemotherapy, thereby improve the curative effect. METHOD: By the methods of less dosage of chemotherapy PFC, give the mouse cisplatin 3 mg x kg(-1) i.p., once a week; CTX and 5-FU 3 mg x kg(-1) i.g. four weeks, set up the mice models of multi-drug resistance of S180 tumor cell, and then observe the P170, Fas, CD54 and apoposis by flow cytometry. RESULT: Tetrandrine can obviously lower the express of P170 increase the express of Fas and the apoposis of drug resistant tumor cell. And at the same time it can obviously reduce the express of intercellular adhesion molecule (CD54). CONCLUSION: Terandrine, with its adjustment of correlated biotic active matter, can intervene the occurrence of the multi-drug resistance of tumor cells induced by chemotherapy.