论文标题
SUPR：稀疏的统一零件人类代表
SUPR: A Sparse Unified Part-Based Human Representation
论文作者
论文摘要
统计3D形状模型的头部，手和完整体被广泛用于计算机视觉和图形中。尽管它们广泛使用，但我们表明，现有的头和手模型无法捕获这些部分的全部运动范围。此外，现有的工作在很大程度上忽略了脚，这对于对人类运动进行建模至关重要，并且在生物力学，动画和鞋类行业中应用。问题在于，使用与各个部分隔离的3D扫描对以前的身体部件模型进行了训练。这样的数据无法捕获此类部分的全部运动范围，例如头部相对于脖子的运动。我们的观察是，全身扫描提供了有关身体部位运动的重要信息。因此，我们提出了一种新的学习方案，该方案使用联合的全身和身体零件扫描共同训练全身模型和特定零件模型。具体而言，我们训练一种称为SUPR的表现力人体模型（稀疏统一零件的人体代表），其中每个关节都严格影响一组稀疏的模型顶点。分解的表示可以将SUPR分为整个身体部位模型。请注意，脚很少受到关注，现有的3D车身模型的脚部高度不足。使用新颖的4D扫描，我们用延伸的运动树训练模型，该模型捕获了脚趾的运动范围。另外，由于接地接触而导致脚变形。为了建模，我们包括一种新型的非线性变形函数，该功能可以预测在脚姿势，形状和地面接触条件下的脚变形。我们对SUPR进行了空前的扫描：120万具体，头部，手和脚扫描。我们定量比较了SUPR和分离的身体部位，发现我们的模型套件比现有模型更好地概括了。 SUPR可从http://supr.is.tue.mpg.de获得
Statistical 3D shape models of the head, hands, and fullbody are widely used in computer vision and graphics. Despite their wide use, we show that existing models of the head and hands fail to capture the full range of motion for these parts. Moreover, existing work largely ignores the feet, which are crucial for modeling human movement and have applications in biomechanics, animation, and the footwear industry. The problem is that previous body part models are trained using 3D scans that are isolated to the individual parts. Such data does not capture the full range of motion for such parts, e.g. the motion of head relative to the neck. Our observation is that full-body scans provide important information about the motion of the body parts. Consequently, we propose a new learning scheme that jointly trains a full-body model and specific part models using a federated dataset of full-body and body-part scans. Specifically, we train an expressive human body model called SUPR (Sparse Unified Part-Based Human Representation), where each joint strictly influences a sparse set of model vertices. The factorized representation enables separating SUPR into an entire suite of body part models. Note that the feet have received little attention and existing 3D body models have highly under-actuated feet. Using novel 4D scans of feet, we train a model with an extended kinematic tree that captures the range of motion of the toes. Additionally, feet deform due to ground contact. To model this, we include a novel non-linear deformation function that predicts foot deformation conditioned on the foot pose, shape, and ground contact. We train SUPR on an unprecedented number of scans: 1.2 million body, head, hand and foot scans. We quantitatively compare SUPR and the separated body parts and find that our suite of models generalizes better than existing models. SUPR is available at http://supr.is.tue.mpg.de
