In this era, the success of large language models and text-to-image models
can be attributed to the driving force of large-scale datasets. However, in the
realm of 3D vision, while remarkable progress has been made with models trained
on large-scale synthetic and real-captured object data like Objaverse and
MVImgNet, a similar level of progress has not been observed in the domain of
human-centric tasks partially due to the lack of a large-scale human dataset.
Existing datasets of high-fidelity 3D human capture continue to be mid-sized
due to the significant challenges in acquiring large-scale high-quality 3D
human data. To bridge this gap, we present MVHumanNet, a dataset that comprises
multi-view human action sequences of 4,500 human identities. The primary focus
of our work is on collecting human data that features a large number of diverse
identities and everyday clothing using a multi-view human capture system, which
facilitates easily scalable data collection. Our dataset contains 9,000 daily
outfits, 60,000 motion sequences and 645 million frames with extensive
annotations, including human masks, camera parameters, 2D and 3D keypoints,
SMPL/SMPLX parameters, and corresponding textual descriptions. To explore the
potential of MVHumanNet in various 2D and 3D visual tasks, we conducted pilot
studies on view-consistent action recognition, human NeRF reconstruction,
text-driven view-unconstrained human image generation, as well as 2D
view-unconstrained human image and 3D avatar generation. Extensive experiments
demonstrate the performance improvements and effective applications enabled by
the scale provided by MVHumanNet. As the current largest-scale 3D human
dataset, we hope that the release of MVHumanNet data with annotations will
foster further innovations in the domain of 3D human-centric tasks at scale.

该研究论文介绍了一个名为 MVHumanNet 的大规模 3D 人体数据集，其中包含 4500 个人的多视角行动序列，以及 9000 个日常服装、60000 个运动序列和 6.45 亿帧的详细注释，通过该数据集的使用，进行了多个 2D 和 3D 视觉任务的实验，展示了性能的提升和有效应用的可能性。

MVHumanNet: 一个大规模的多视图日常穿着人类捕捉数据集

MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human  Captures

The development of algorithms that learn behavioral driving models using
human demonstrations has led to increasingly realistic simulations. In general,
such models learn to jointly predict trajectories for all controlled agents by
exploiting road context information such as drivable lanes obtained from
manually annotated high-definition (HD) maps. Recent studies show that these
models can greatly benefit from increasing the amount of human data available
for training. However, the manual annotation of HD maps which is necessary for
every new location puts a bottleneck on efficiently scaling up human traffic
datasets. We propose a drone birdview image-based map (DBM) representation that
requires minimal annotation and provides rich road context information. We
evaluate multi-agent trajectory prediction using the DBM by incorporating it
into a differentiable driving simulator as an image-texture-based
differentiable rendering module. Our results demonstrate competitive
multi-agent trajectory prediction performance when using our DBM representation
as compared to models trained with rasterized HD maps.

利用无人机鸟瞰图像构建的地图表示法可以有效地提供路况信息，实现了在多智能体轨迹预测中获得与高清地图相当的性能。

无需高清地图：直接从无人机图像预测驾驶行为

Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone  Images

Reinforcement learning has been widely adopted to model dialogue managers in
task-oriented dialogues. However, the user simulator provided by
state-of-the-art dialogue frameworks are only rough approximations of human
behaviour. The ability to learn from a small number of human interactions is
hence crucial, especially on multi-domain and multi-task environments where the
action space is large. We therefore propose to use structured policies to
improve sample efficiency when learning on these kinds of environments. We also
evaluate the impact of learning from human vs simulated experts. Among the
different levels of structure that we tested, the graph neural networks (GNNs)
show a remarkable superiority by reaching a success rate above 80% with only 50
dialogues, when learning from simulated experts. They also show superiority
when learning from human experts, although a performance drop was observed,
indicating a possible difficulty in capturing the variability of human
strategies. We therefore suggest to concentrate future research efforts on
bridging the gap between human data, simulators and automatic evaluators in
dialogue frameworks.

本研究旨在探讨使用结构化政策提高在多领域和多任务环境下的强化学习样本效率。作者在测试不同结构化水平时，发现图形神经网络具有优势，且建议未来的研究应聚焦于连接人类数据、模拟器和自动评估器。