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We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals. At ...

We present SinGRAV, an attempt to learn a generative radiance volume from multi-view observations of a single natural scene, in stark contrast to existing category-level 3D generative models that learn from images of many object-centric scenes. ...

We present C · ASE, an efficient and effective framework that learns Conditional Adversarial Skill Embeddings for physics-based characters. C · ASE enables the physically simulated character to learn a diverse repertoire of skills while providing ...

We address the problem of scene-aware activity program generation, which requires decomposing a given activity task into instructions that can be sequentially performed within a target scene to complete the activity. While existing methods have shown the ...

The internet of things (IoT) has become a key support object for Chinese strategic emerging industries. It is of great practical significance to promote the construction of the internet of things. Driven by national policies, the development of the ...

We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual ...

It has become an important issue for digital startups to figure out how to realize value creation and value capture in open innovation. Using data from the CPSED II database, we examined the impact of digital startups' appropriability mechanisms on firm ...

Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. These methods, however, only complete the partial shape with a single output, ...

We introduce a computational solution for cost-efficient 3D fabrication using universal building blocks. Our key idea is to employ a set of universal blocks, which can be massively prefabricated at a low cost, to quickly assemble and constitute a ...

We pose the decompose-and-pack or DAP problem, which tightly combines shape decomposition and packing. While in general, DAP seeks to decompose an input shape into a small number of parts which can be efficiently packed, our focus is geared towards 3D ...

This paper presents a reconstruction formula in general shift-invariant signal spaces that improve the rate of A-P iterative algorithm. We use the algorithm to show reconstruction of signals from weighted samples and also show that there is better ...

The emergence of low-cost 3D printers steers the investigation of new geometric problems that control the quality of the fabricated object. In this paper, we present a method to reduce the material cost and weight of a given object while providing a ...

A Graph-optimized Linear Discriminant Analysis (GLDA) for face recognition is proposed, which redefine the intrinsic and penalty graph and trade off the importance degrees of the same-class points to the intrinsic graph and the importance degrees of the ...

To efficiently resolve classification rule mining problem, a novel classification rule mining algorithm is proposed by using Locality pursuit projection (LPP) and memetic algorithm (MA) in this paper. Experimental results show that the proposed ...