An efficient self-attention mechanism, that is, cross-covariance attention, was utilized across our framework to perceive the correlations between points at different distances. Specifically, the transformer encoder extracts the target shape's local geometry details for identity attributes and the source...
Encoder由多头自注意力层(multi-headself-attention layer)与前馈神经网络(feed-forward network)组成,Decoder则由多头自注意力层、编码器解码器交叉注意力层和前馈神经网络构成。2.2 点代理NLP中的transformer以一个一维的单词嵌入序列作为输入,为了使三维点云适合于变压器,第一步是将点云转换为一系列向量序列。一个简...
Sparse tokens representing occupied voxels are further processed through a Reconstruction Transformer that employs self-attention and deformable cross-attention mechanisms to refine geometry and retrieve texture details with 3D to 2D projection. Finally, the refined 3D tokens are converted into 3D Gaussian...
Network Attention Visualization Other tools FAQ License Documentation Setup MapNet uses a Conda environment that makes it easy to install all dependencies. Installminicondawith Python 2.7. Create themapnetConda environment:conda env create -f environment.yml. ...
解码器的几何感知结构和编码器一样,只是因为多了输入序列,因此对序列部分也要做一个几何感知,毕竟要整合两个 Attention: defforward(self,q,v,self_knn_index=None,cross_knn_index=None):norm_q=self.norm1(q)q_1=self.self_attn(norm_q)ifself_knn_indexisnotNone:knn_f=get_graph_feature(norm_q,se...
In order to obtain geometric representation ability, two novel geometry-aware architectures are designed respectively for the encoder and decoder in our GAT by 鈪 ) a geometry gate-controlled self-attention refiner, and 鈪 ) a group of position-LSTMs. The first one explicitly incorporates relative...
Specifically, the GACN can complete a pose-invariant image editing task with long-range dependency by introducing conditional self-attention operations to a generative adversarial network . Moreover, the GACN presents non-local operations as building blocks of the classifier to capture the texture ...
Furthermore, we use a geometry-aware attention mechanism consisting of two feature attention modules to address the issue of self-occlusion in sparse view inputs, resulting in improved body shape details and reduced blurriness. Qualitative and quantitative results on the ZJU-MoCap and Thuman ...
which captures spatial occupancy from hierarchical image features extracted using a combination of a convolutional layer and DINOv2. Sparse tokens representing occupied voxels are further processed through a Reconstruction Transformer that employs self-attention and deformable cross-attention mechanisms to refi...