Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying "tree" pixels indicates that pixels above and to the sides are more likely to be "sky" whereas pixels below are more likely to be "grass." Incorporating such global information across the entire image and between all classes is a computational challenge as it is image-dependent, and hence, cannot be precomputed.In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine this with appearance-based features to provide a final segmentation. We compare our results to recent published results on several multiclass image segmentation databases and show that the incorporation of relative location information allows us to significantly outperform the current state-of-the-art.

Figure 1: Animation of a motion capture sequence taken for a subject, of whom we have a single body scan. The muscle deformations are synthesized automatically from the space of pose and body shape deformations. AbstractWe introduce the SCAPE method (Shape Completion and Animation for PEople) -a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations. We learn a pose deformation model that derives the non-rigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion -generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.

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We address the problem of detecting complex articulated objects and their pose in 3D range scan data. This task is very difficult when the orientation of the object is unknown, and occlusion and clutter are present in the scene. To address the problem, we design an efficient probabilistic framework, based on the articulated model of an object, which combines multiple information sources. Our framework enforces that the surfaces and edge discontinuities of model parts are matched well in the scene while respecting the rules of occlusion, that joint constraints and angles are maintained, and that object parts don't intersect. Our approach starts by using low-level detectors to suggest part placement hypotheses. In a hypothesis enrichment phase, these original hypotheses are used to generate likely placement suggestions for their neighboring parts. The probabilities over the possible part placement configurations are computed using efficient OpenGL rendering. Loopy belief propagation is used to optimize the resulting Markov network to obtain the most likely object configuration, which is additionally refined using an Iterative Closest Point algorithm adapted for articulated models. Our model is tested on several datasets, where we demonstrate successful pose detection for models consisting of 15 parts or more, even when the object is seen from different viewpoints, and various occluding objects and clutter are present in the scene.

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