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 https://github.com/JaehaKim97/BlurHand_RELEASE.

翻译：手部作为人体最活跃的肢体部位之一，其快速运动常导致图像模糊。然而，现有三维手部网格重建方法主要针对清晰图像开展研究，由于缺乏包含模糊手部图像的数据集，尚未考虑运动模糊的影响。本文首先提出全新数据集BlurHand，其中包含带有三维标注的真值图像。该数据集通过从清晰手部图像序列合成运动模糊构建，能够模拟真实自然的运动模糊效果。除数据集外，本文还提出基线网络BlurHandNet，实现从模糊手部图像中精确重建三维手部网格。与现有方法输出静态单帧手部网格不同，我们的BlurHandNet可将模糊输入图像展开为三维手部网格序列，从而充分利用模糊图像中的时序信息。实验证明BlurHand数据集对模糊图像三维网格重建的有效性，所提BlurHandNet在模糊图像上产生更鲁棒的结果，且能良好泛化到自然场景图像。训练代码与BlurHand数据集已开源至https://github.com/JaehaKim97/BlurHand_RELEASE。