Grasping is an important human activity that has long been studied in
robotics, computer vision, and cognitive science. Most existing works study
grasping from the perspective of synthesizing hand poses conditioned on 3D or
2D object representations. We propose GenHeld to address the inverse problem of
synthesizing held objects conditioned on 3D hand model or 2D image. Given a 3D
model of hand, GenHeld 3D can select a plausible held object from a large
dataset using compact object representations called object codes.The selected
object is then positioned and oriented to form a plausible grasp without
changing hand pose. If only a 2D hand image is available, GenHeld 2D can edit
this image to add or replace a held object. GenHeld 2D operates by combining
the abilities of GenHeld 3D with diffusion-based image editing. Results and
experiments show that we outperform baselines and can generate plausible held
objects in both 2D and 3D. Our experiments demonstrate that our method achieves
high quality and plausibility of held object synthesis in both 3D and 2D.

Grasping 主题的研究通过 GenHeld 方法实现了在 3D 或 2D 条件下合成被握住的物体，包括选择、定位和编辑操作，结果展示了高质量和可行性。

GenHeld: 生成和编辑手持物体

GenHeld: Generating and Editing Handheld Objects

This research work seeks to explore and identify strategies that can
determine road topology information in 2D and 3D under highly dynamic urban
driving scenarios. To facilitate this exploration, we introduce a substantial
dataset comprising nearly one million automatically labeled data frames. A key
contribution of our research lies in developing an automatic label-generation
process and an occlusion handling strategy. This strategy is designed to model
a wide range of occlusion scenarios, from mild disruptions to severe blockages.
Furthermore, we present a comprehensive ablation study wherein multiple
centerline detection methods are developed and evaluated. This analysis not
only benchmarks the performance of various approaches but also provides
valuable insights into the interpretability of these methods. Finally, we
demonstrate the practicality of our methods and assess their adaptability
across different sensor configurations, highlighting their versatility and
relevance in real-world scenarios. Our dataset and experimental models are
publicly available.

本研究旨在探索和确定在高度动态的城市驾驶场景下确定 2D 和 3D 的道路拓扑信息的策略。为了促进这一探索，我们引入了一个庞大的数据集，包含近 100 万个自动标记的数据帧。我们的研究的一个重要贡献在于开发了一个自动生成标签的过程和一个遮挡处理策略，该策略旨在模拟从轻微的中断到严重的阻塞的各种遮挡场景。此外，我们进行了全面的消融研究，开发和评估了多种中心线检测方法。这种分析不仅评估了各种方法的性能，还提供了宝贵的对这些方法可解释性的见解。最后，我们演示了我们的方法的实用性，并评估了它们在不同传感器配置下的适应性，突出了它们在实际场景中的多功能性和相关性。我们的数据集和实验模型是公开可用的。