1,956 research outputs found

    Synthesizing animations of human manipulation tasks

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    Hierarchical Planning and Control for Box Loco-Manipulation

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    Humans perform everyday tasks using a combination of locomotion and manipulation skills. Building a system that can handle both skills is essential to creating virtual humans. We present a physically-simulated human capable of solving box rearrangement tasks, which requires a combination of both skills. We propose a hierarchical control architecture, where each level solves the task at a different level of abstraction, and the result is a physics-based simulated virtual human capable of rearranging boxes in a cluttered environment. The control architecture integrates a planner, diffusion models, and physics-based motion imitation of sparse motion clips using deep reinforcement learning. Boxes can vary in size, weight, shape, and placement height. Code and trained control policies are provided

    Toward a computational theory for motion understanding: The expert animators model

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    Artificial intelligence researchers claim to understand some aspect of human intelligence when their model is able to emulate it. In the context of computer graphics, the ability to go from motion representation to convincing animation should accordingly be treated not simply as a trick for computer graphics programmers but as important epistemological and methodological goal. In this paper we investigate a unifying model for animating a group of articulated bodies such as humans and robots in a three-dimensional environment. The proposed model is considered in the framework of knowledge representation and processing, with special reference to motion knowledge. The model is meant to help setting the basis for a computational theory for motion understanding applied to articulated bodies

    Programmatic and Direct Manipulation, Together at Last

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    Direct manipulation interfaces and programmatic systems have distinct and complementary strengths. The former provide intuitive, immediate visual feedback and enable rapid prototyping, whereas the latter enable complex, reusable abstractions. Unfortunately, existing systems typically force users into just one of these two interaction modes. We present a system called Sketch-n-Sketch that integrates programmatic and direct manipulation for the particular domain of Scalable Vector Graphics (SVG). In Sketch-n-Sketch, the user writes a program to generate an output SVG canvas. Then the user may directly manipulate the canvas while the system immediately infers a program update in order to match the changes to the output, a workflow we call live synchronization. To achieve this, we propose (i) a technique called trace-based program synthesis that takes program execution history into account in order to constrain the search space and (ii) heuristics for dealing with ambiguities. Based on our experience with examples spanning 2,000 lines of code and from the results of a preliminary user study, we believe that Sketch-n-Sketch provides a novel workflow that can augment traditional programming systems. Our approach may serve as the basis for live synchronization in other application domains, as well as a starting point for yet more ambitious ways of combining programmatic and direct manipulation.Comment: PLDI 2016 Paper + Supplementary Appendice

    Emotion Transfer for Hand Animation

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    We propose a new data-driven framework for synthesizing hand motion at different emotion levels. Specifically, we first capture high-quality hand motion using VR gloves. The hand motion data is then annotated with the emotion type and a latent space is constructed from the motions to facilitate the motion synthesis process. By interpolating the latent representation of the hand motion, new hand animation with different levels of emotion strength can be generated. Experimental results show that our framework can produce smooth and consistent hand motions at an interactive rate
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