Hands, one of the most dynamic parts of our body, suffer from blur due to
their active movements. However, previous 3D hand mesh recovery methods have
mainly focused on sharp hand images rather than considering blur due to the
absence of datasets providing blurry hand images. We first present a novel
dataset BlurHand, which contains blurry hand images with 3D groundtruths. The
BlurHand is constructed by synthesizing motion blur from sequential sharp hand
images, imitating realistic and natural motion blurs. In addition to the new
dataset, we propose BlurHandNet, a baseline network for accurate 3D hand mesh
recovery from a blurry hand image. Our BlurHandNet unfolds a blurry input image
to a 3D hand mesh sequence to utilize temporal information in the blurry input
image, while previous works output a static single hand mesh. We demonstrate
the usefulness of BlurHand for the 3D hand mesh recovery from blurry images in
our experiments. The proposed BlurHandNet produces much more robust results on
blurry images while generalizing well to in-the-wild images. The training codes
and BlurHand dataset are available at
this https URL

本文在构建含真实并自然运动模糊的手图像数据集的基础上，提出一种解决模糊手图像建立 3D 手掌模型的算法 BlurHandNet，并表明这种方法在解决这个问题方面的实用性和鲁棒性。

从模糊图像中恢复三维手部网格序列：一个新的数据集和时间展开

Recovering 3D Hand Mesh Sequence from a Single Blurry Image: A New  Dataset and Temporal Unfolding

Recent years have witnessed significant progress in 3D hand mesh recovery.
Nevertheless, because of the intrinsic 2D-to-3D ambiguity, recovering
camera-space 3D information from a single RGB image remains challenging. To
tackle this problem, we divide camera-space mesh recovery into two sub-tasks,
i.e., root-relative mesh recovery and root recovery. First, joint landmarks and
silhouette are extracted from a single input image to provide 2D cues for the
3D tasks. In the root-relative mesh recovery task, we exploit semantic
relations among joints to generate a 3D mesh from the extracted 2D cues. Such
generated 3D mesh coordinates are expressed relative to a root position, i.e.,
wrist of the hand. In the root recovery task, the root position is registered
to the camera space by aligning the generated 3D mesh back to 2D cues, thereby
completing cameraspace 3D mesh recovery. Our pipeline is novel in that (1) it
explicitly makes use of known semantic relations among joints and (2) it
exploits 1D projections of the silhouette and mesh to achieve robust
registration. Extensive experiments on popular datasets such as FreiHAND, RHD,
and Human3.6M demonstrate that our approach achieves stateof-the-art
performance on both root-relative mesh recovery and root recovery. Our code is
publicly available at this https URL

该研究介绍了一种利用已知关节点之间语义关系和 1D 投影来恢复单个 RGB 图像的相机空间 3D 手部网格的方法，并在流行的数据集上展示了该方法在根相对网格恢复和根恢复方面的最新成果。