3D-aware Generative Adversarial Networks (3D-GANs) currently exhibit
artifacts in their 3D geometrical modeling, such as mesh imperfections and
holes. These shortcomings are primarily attributed to the limited availability
of annotated 3D data, leading to a constrained "valid latent area" for
satisfactory modeling. To address this, we present a Self-Supervised Learning
(SSL) technique tailored as an auxiliary loss for any 3D-GAN, designed to
improve its 3D geometrical modeling capabilities. Our approach pioneers an
inversion technique for 3D-GANs, integrating an encoder that performs adaptive
spatially-varying range operations. Utilizing this inversion, we introduce the
Cyclic Generative Constraint (CGC), aiming to densify the valid latent space.
The CGC operates via augmented local latent vectors that maintain the same
geometric form, and it imposes constraints on the cycle path outputs,
specifically the generator-encoder-generator sequence. This SSL methodology
seamlessly integrates with the inherent GAN loss, ensuring the integrity of
pre-existing 3D-GAN architectures without necessitating alterations. We
validate our approach with comprehensive experiments across various datasets
and architectures, underscoring its efficacy. Our project website:
this https URL

通过自监督学习技术，我们提出了一种适用于任何 3D-GAN 的辅助损失，旨在改进其 3D 几何建模能力，并通过综合实验证实了该方法的有效性。

提升几何建模能力的自监督学习在三维感知生成对抗网络中的应用

Self-supervised Learning for Enhancing Geometrical Modeling in 3D-Aware  Generative Adversarial Network

In order to deal with the sparse and unstructured raw point clouds, LiDAR
based 3D object detection research mostly focuses on designing dedicated local
point aggregators for fine-grained geometrical modeling. In this paper, we
revisit the local point aggregators from the perspective of allocating
computational resources. We find that the simplest pillar based models perform
surprisingly well considering both accuracy and latency. Additionally, we show
that minimal adaptions from the success of 2D object detection, such as
enlarging receptive field, significantly boost the performance. Extensive
experiments reveal that our pillar based networks with modernized designs in
terms of architecture and training render the state-of-the-art performance on
the two popular benchmarks: Waymo Open Dataset and nuScenes. Our results
challenge the common intuition that the detailed geometry modeling is essential
to achieve high performance for 3D object detection.

本文提出了基于 pillar 的算法模型在 LiDAR 3D 目标检测中的效果以及在架构和训练等方面的现代化设计，通过扩大感受野等方法取得了显著提升，在 Waymo 开放数据集和 nuScenes 数据集上实现了最优性能。

PillarNeXt: 针对 LiDAR 点云的 3D 物体检测重新思考网络设计

PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR  Point Clouds

The end-to-end Human Mesh Recovery (HMR) approach has been successfully used
for 3D body reconstruction. However, most HMR-based frameworks reconstruct
human body by directly learning mesh parameters from images or videos, while
lacking explicit guidance of 3D human pose in visual data. As a result, the
generated mesh often exhibits incorrect pose for complex activities. To tackle
this problem, we propose to exploit 3D pose to calibrate human mesh.
Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial
PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in
a serial or parallel manner, these two frameworks can effectively correct human
mesh with guidance of a concise pose calibration module. Furthermore, since the
calibration module is designed via non-rigid pose transformation, our PC-HMR
frameworks can flexibly tackle bone length variations to alleviate misplacement
in the calibrated mesh. Finally, our frameworks are based on generic and
complementary integration of data-driven learning and geometrical modeling. Via
plug-and-play modules, they can be efficiently adapted for both
image/video-based human mesh recovery. Additionally, they have no requirement
of extra 3D pose annotations in the testing phase, which releases inference
difficulties in practice. We perform extensive experiments on the popular
bench-marks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks
achieve the SOTA results.

本文提出了两种新颖的姿态校准框架，采用先进的 3D 姿态估计器和 HMR 序列或并行方式有效地校准人体网格，通过非刚性姿态变换，可以灵活地处理骨长变化以缓解校准后的网格中的误放置，并通过数据驱动学习和几何建模的通用和互补集成来实现，可用于各种图像 / 视频人体网格的恢复，其在多项基准测试中取得了最新的表现。