Zobrazeno 1 - 10
of 3 426
pro vyhledávání: '"WANG, BEIBEI"'
Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g., nodes, edges, node attributes
Externí odkaz:
http://arxiv.org/abs/2410.21749
3D Gaussian Splatting (3DGS) has shown impressive results for the novel view synthesis task, where lighting is assumed to be fixed. However, creating relightable 3D assets, especially for objects with ill-defined shapes (fur, fabric, etc.), remains a
Externí odkaz:
http://arxiv.org/abs/2409.19702
Autor:
Li, Zixuan, Shen, Pengfei, Sun, Hanxiao, Zhang, Zibo, Guo, Yu, Liu, Ligang, Yan, Ling-Qi, Marschner, Steve, Hasan, Milos, Wang, Beibei
Accurately rendering the appearance of fabrics is challenging, due to their complex 3D microstructures and specialized optical properties. If we model the geometry and optics of fabrics down to the fiber level, we can achieve unprecedented rendering
Externí odkaz:
http://arxiv.org/abs/2409.06368
Graph Convolutional Networks (GCNs) have been widely studied. The core of GCNs is the definition of convolution operators on graphs. However, existing Graph Convolution (GC) operators are mainly defined on adjacency matrix and node features and gener
Externí odkaz:
http://arxiv.org/abs/2406.14846
In recent years, graph prompt learning/tuning has garnered increasing attention in adapting pre-trained models for graph representation learning. As a kind of universal graph prompt learning method, Graph Prompt Feature (GPF) has achieved remarkable
Externí odkaz:
http://arxiv.org/abs/2406.10498
3D Gaussian Splatting (3DGS) has shown a powerful capability for novel view synthesis due to its detailed expressive ability and highly efficient rendering speed. Unfortunately, creating relightable 3D assets with 3DGS is still problematic, particula
Externí odkaz:
http://arxiv.org/abs/2406.18544
Measured Bidirectional Texture Function (BTF) can faithfully reproduce a realistic appearance but is costly to acquire and store due to its 6D nature (2D spatial and 4D angular). Therefore, it is practical and necessary for rendering to synthesize BT
Externí odkaz:
http://arxiv.org/abs/2405.14025
Digitizing woven fabrics would be valuable for many applications, from digital humans to interior design. Previous work introduces a lightweight woven fabric acquisition approach by capturing a single reflection image and estimating the fabric parame
Externí odkaz:
http://arxiv.org/abs/2406.19398
Woven fabrics are widely used in applications of realistic rendering, where real-time capability is also essential. However, rendering realistic woven fabrics in real time is challenging due to their complex structure and optical appearance, which ca
Externí odkaz:
http://arxiv.org/abs/2406.17782
Proximity detection in indoor environments based on WiFi signals has gained significant attention in recent years. Existing works rely on the dynamic signal reflections and their extracted features are dependent on motion strength. To address this is
Externí odkaz:
http://arxiv.org/abs/2404.19182