当服务器有可视化界面，直接起飞！
最新效果demo展现：
标题：
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/huawei-noah/noah-research/tree/master/CLIFF
自顶向下的办法在3D人体姿态和形状估量范畴占有主导地位，由于它们与人体检测别离，答应研究人员专心于核心问题。但是，裁剪是它们的第一步，从一开端就丢掉了方位信息，这使得它们无法在原始相机坐标系中准确猜测大局旋转。为了处理这个问题，咱们主张在这个使命中带着全帧方位信息（CLIFF）。具体来说，咱们经过将裁剪的图画特征与其鸿沟框信息连接起来，向CLIFF供给更全面的特征。咱们在更宽的全帧视界下核算2D重投影丢失，选用与在图画中投影的人类似的投影进程。CLIFF由全球方位感知信息供给并监督，它直接猜测全球旋转以及更准确的关节姿态。此外，咱们提出了一种根据CLIFF的伪地上真值注释器，它为户外二维数据集供给高质量的三维注释，并为根据回归的办法供给要害的全面监督。对盛行基准测验的很多试验标明，CLIFF的体现显着优于现有技能，并在AGORA排行榜上排名第一（SMPL算法盯梢）。
最新论文收拾
ECCV2022
Updated on : 20 Oct 2022
total number : 7
-
论文/Paper:
http://arxiv.org/pdf/2210.10770 -
代码/Code: None
GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs
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论文/Paper:
http://arxiv.org/pdf/2210.10758 -
代码/Code: None
Attaining Class-level Forgetting in Pretrained Model using Few Samples
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论文/Paper:
http://arxiv.org/pdf/2210.10670 -
代码/Code: None
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
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论文/Paper:
http://arxiv.org/pdf/2210.10633 -
代码/Code: None
PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting
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论文/Paper:
http://arxiv.org/pdf/2210.10542 -
代码/Code: None
PERI: Part Aware Emotion Recognition In The Wild
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论文/Paper:
http://arxiv.org/pdf/2210.10130 -
代码/Code: None
Anomaly Detection Requires Better Representations
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论文/Paper:
http://arxiv.org/pdf/2210.10773 -
代码/Code: None
CVPR2022
NeurIPS
Updated on : 20 Oct 2022
total number : 5
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
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论文/Paper:
http://arxiv.org/pdf/2210.10775 -
代码/Code:
https://github.com/AIR-DISCOVER/TOIST
Learning to Discover and Detect Objects
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论文/Paper:
http://arxiv.org/pdf/2210.10774 -
代码/Code: None
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
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论文/Paper:
http://arxiv.org/pdf/2210.10716 -
代码/Code: None
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes
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论文/Paper:
http://arxiv.org/pdf/2210.10431 -
代码/Code: None
On the Adversarial Robustness of Mixture of Experts
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论文/Paper:
http://arxiv.org/pdf/2210.10253 -
代码/Code: None
