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Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus on learning overall visual cues of a scene but disregard a holistic view of human–object relationships and interactions, that is, how a human interacts with respect to short-term task for completion and long-term goal. We therefore argue to improve human action recognition by exploiting both the local and global contexts of human–object interactions (HOIs). In this paper, we propose the Global-Local Interaction Distillation Network (GLIDN), learning human and object interactions through space and time via knowledge distillation for holistic HOI understanding. GLIDN encodes humans and objects into graph nodes and learns local and global relations via graph attention network. The local context graphs learn the relation between humans and objects at a frame level by capturing their co-occurrence at a specific time step. The global relation graph is constructed based on the video-level of human and object interactions, identifying their long-term relations throughout a video sequence. We also investigate how knowledge from these graphs can be distilled to their counterparts for improving HOI recognition. Finally, we evaluate our model by conducting comprehensive experiments on two datasets including Charades and CAD-120. Our method outperforms the baselines and counterpart approaches.

To effectively control particle‐based cloud evolution without imposing strict position constraints, we propose a novel method integrating a control force field and a phase transition control into the position‐based fluids (PBF) framework. To produce realistic cloud simulation, we incorporate both fluid dynamics and thermodynamics to govern cloud particle movement. The fluid dynamics is simulated through our novel driving and damping force terms. As these terms are only formulated based on cloud particle density and position, they simplify the inputs and make our method free from artificial positional constraints. The thermodynamics is implemented by our phase transition control, which can effectively simulate cloud evolution between discrepant initial and target shapes, producing plausible results. Uniquely, our method can also support target shape change during cloud simulation. Experiment results have demonstrated our method surpasses existing methods.

Dynamic mesh sequence (DMS) is a simple and accurate representation for precisely recording a 3D animation sequence. Despite its simplicity, this representation is typically large in data size, making storage and transmission expensive. This paper presents a novel framework that allows effective DMS compression and progressive streaming by eliminating spatial and temporal redundancy. To explore temporal redundancy, we propose a temporal frame‐clustering algorithm to organize DMS frames by their motion trajectory changes, eliminating intracluster redundancy by principal component analysis dimensionality reduction. To eliminate spatial redundancy, we propose an algorithm to transform the coordinates of mesh vertex trajectory into a decorrelated trajectory space, generating a new spatially nonredundant trajectory representation. We finally apply a spectral graph wavelet transform with color set partitioning embedded block encoding to turn the resultant DMS into a multiresolution representation to support progressive streaming. Experiment results show that our method outperforms several existing methods in terms of storage requirement and reconstruction quality.

Deformable objects can be used to model soft objects such as clothing, human faces and animal characters. They are important as they can improve the realism of the applications. However, most existing hardware accelerators cannot render deformable objects directly. A tessellation process is often used to convert a deformable object into polygons so that the hardware graphics accelerator may render them. Unfortunately, this tessellation process is computationally very expensive. While the object is deforming, the tessellation process needs to be performed repeatedly to convert the deforming objects into polygons. As a result, deformable objects are seldom used in real-time applications such as virtual environments and computer games. Since trimmed NURBS surfaces are often used to represent deformable objects, in this paper we present an efficient method for incremental rendering of deformable trimmed NURBS surfaces. A trimmed NURBS surface typically deforms through the deformation of the trimmed NURBS surface and/or the trimming curve. Our method handles both trimmed surface deformation as well as trimming curve deformation. Experimental results show that our method performs significantly faster than the method used in OpenGL and can be used in real-time applications, such as computer games.