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Abstract. We consider the flow of marine-terminating outlet glaciers that are laterally confined in a channel of prescribed width. In that case, the drag exerted by the channel side walls on a floating ice shelf can reduce extensional stress at the grounding line. If ice flux through the grounding line increases with both ice thickness and extensional stress, then a longer shelf can reduce ice flux by decreasing extensional stress. Consequently, calving has an effect on flux through the grounding line by regulating the length of the shelf. In the absence of a shelf, it plays a similar role by controlling the above-flotation height of the calving cliff. Using two calving laws, one due to Nick et al. (2010) based on a model for crevasse propagation due to hydrofracture and the other simply asserting that calving occurs where the glacier ice becomes afloat, we pose and analyse a flowline model for a marine-terminating glacier by two methods: direct numerical solution and matched asymptotic expansions. The latter leads to a boundary layer formulation that predicts flux through the grounding line as a function of depth to bedrock, channel width, basal drag coefficient, and a calving parameter. By contrast with unbuttressed marine ice sheets, we find that flux can decrease with increasing depth to bedrock at the grounding line, reversing the usual stability criterion for steady grounding line location. Stable steady states can then have grounding lines located on retrograde slopes. We show how this anomalous behaviour relates to the strength of lateral versus basal drag on the grounded portion of the glacier and to the specifics of the calving law used.

Figure 1: Left to right: an actor performing in the capture setup; one of sixteen views from the camera array; reconstructed T-shirt geometry; the real T-shirt is replaced by a rendering of the captured geometry with different appearance characteristics. AbstractA lot of research has recently focused on the problem of capturing the geometry and motion of garments. Such work usually relies on special markers printed on the fabric to establish temporally coherent correspondences between points on the garment's surface at different times. Unfortunately, this approach is tedious and prevents the capture of off-the-shelf clothing made from interesting fabrics.In this paper, we describe a marker-free approach to capturing garment motion that avoids these downsides. We establish temporally coherent parameterizations between incomplete geometries that we extract at each timestep with a multiview stereo algorithm. We then fill holes in the geometry using a template. This approach, for the first time, allows us to capture the geometry and motion of unpatterned, off-the-shelf garments made from a range of different fabrics.

In this paper we present a learned alternative to the Motion Matching algorithm which retains the positive properties of Motion Matching but additionally achieves the scalability of neural-network-based generative models. Although neural-network-based generative models for character animation are capable of learning expressive, compact controllers from vast amounts of animation data, methods such as Motion Matching still remain a popular choice in the games industry due to their flexibility, predictability, low preprocessing time, and visual quality - all properties which can sometimes be difficult to achieve with neural-network-based methods. Yet, unlike neural networks, the memory usage of such methods generally scales linearly with the amount of data used, resulting in a constant trade-off between the diversity of animation which can be produced and real world production budgets. In this work we combine the benefits of both approaches and, by breaking down the Motion Matching algorithm into its individual steps, show how learned, scalable alternatives can be used to replace each operation in turn. Our final model has no need to store animation data or additional matching meta-data in memory, meaning it scales as well as existing generative models. At the same time, we preserve the behavior of Motion Matching, retaining the quality, control, and quick iteration time which are so important in the industry.