update paper information in doc

open-mmlab · Jan 4, 2022 · a0c396b · a0c396b
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diff --git a/docs/papers/algorithms/awingloss.md → docs/en/papers/algorithms/awingloss.md b/docs/papers/algorithms/awingloss.md → docs/en/papers/algorithms/awingloss.md
diff --git a/docs/papers/algorithms/softwingloss.md → docs/en/papers/algorithms/softwingloss.md b/docs/papers/algorithms/softwingloss.md → docs/en/papers/algorithms/softwingloss.md
@@ -26,5 +26,5 @@ In this paper, we propose a structure-coherent deep feature learning method for
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 <div align=center>
-<img src="https://user-images.githubusercontent.com/15977946/148014510-93149a98-f462-49e7-bc92-7dd50fd90a45.png">
+<img src="https://user-images.githubusercontent.com/15977946/148036579-b128fb21-3a2d-4894-bb54-4223a4cdf856.png">
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diff --git a/docs/en/papers/algorithms/voxelpose.md b/docs/en/papers/algorithms/voxelpose.md
@@ -15,3 +15,15 @@
 ```
 
 </details>
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+## Abstract
+
+<!-- [ABSTRACT] -->
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+We present VoxelPose to estimate 3D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimates, VoxelPose directly operates in the 3D space therefore avoids making incorrect decisions in each camera view. To achieve this goal, features in all camera views are aggregated in the 3D voxel space and fed into Cuboid Proposal Network (CPN) to localize all people. Then we propose Pose Regression Network (PRN) to estimate a detailed 3D pose for each proposal. The approach is robust to occlusion which occurs frequently in practice. Without bells and whistles, it outperforms the previous methods on several public datasets.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/15977946/148036780-b73a878c-7244-480b-b56e-ac739396856b.png">
+</div>
diff --git a/docs/papers/techniques/awingloss.md → docs/en/papers/techniques/awingloss.md b/docs/papers/techniques/awingloss.md → docs/en/papers/techniques/awingloss.md
diff --git a/docs/papers/techniques/softwingloss.md → docs/en/papers/techniques/softwingloss.md b/docs/papers/techniques/softwingloss.md → docs/en/papers/techniques/softwingloss.md
@@ -26,5 +26,5 @@ In this paper, we propose a structure-coherent deep feature learning method for
 <!-- [IMAGE] -->
 
 <div align=center>
-<img src="https://user-images.githubusercontent.com/15977946/148014510-93149a98-f462-49e7-bc92-7dd50fd90a45.png">
+<img src="https://user-images.githubusercontent.com/15977946/148036579-b128fb21-3a2d-4894-bb54-4223a4cdf856.png">
 </div>
diff --git a/docs/papers/algorithms/voxelpose.md b/docs/papers/algorithms/voxelpose.md
diff --git a/docs/papers/datasets/panoptic_body3d.md b/docs/papers/datasets/panoptic_body3d.md