With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image...With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image sets perform well in monocular 3D face reconstruction tasks,they tend to rely on the constraints of the a priori model or the appearance conditions of the input images,fundamentally because of the inability to propose an effective method to reduce the effects of two-dimensional(2D)ambiguity.To solve this problem,we developed an unsupervised training framework for monocular face 3D reconstruction using rotational cycle consistency.Specifically,to learn more accurate facial information,we first used an autoencoder to factor the input images and applied these factors to generate normalized frontal views.We then proceeded through a differentiable renderer to use rotational consistency to continuously perceive refinement.Our method provided implicit multi-view consistency constraints on the pose and depth information estimation of the input face,and the performance was accurate and robust in the presence of large variations in expression and pose.In the benchmark tests,our method performed more stably and realistically than other methods that used 3D face reconstruction in monocular 2D images.展开更多
RGB-Infrared person re-IDentification(re-ID)aims to match RGB and infrared(IR)images of the same person.However,the modality discrepancy between RGB and IR images poses a significant challenge for re-ID.To address thi...RGB-Infrared person re-IDentification(re-ID)aims to match RGB and infrared(IR)images of the same person.However,the modality discrepancy between RGB and IR images poses a significant challenge for re-ID.To address this issue,this paper proposes a Proxy-based Embedding Alignment(PEA)method to align the RGB and IR modalities in the embedding space.PEA introduces modality-specific identity proxies and leverages the sample-to-proxy relations to learn the model.Specifically,PEA focuses on three types of alignments:intra-modality alignment,inter-modality alignment,and cycle alignment.Intra-modality alignment aims to align sample features and proxies of the same identity within a modality.Inter-modality alignment aims to align sample features and proxies of the same identity across different modalities.Cycle alignment requires that a proxy is aligned with itself after tracing it along a cross-modality cycle(e.g.,IR→RGB→IR).By integrating these alignments into the training process,PEA effectively mitigates the impact of modality discrepancy and learns discriminative features across modalities.We conduct extensive experiments on several RGB-IR re-ID datasets,and the results show that PEA outperforms current state-of-the-art methods.Notably,on SYSU-MM01 dataset,PEA achieves 71.0%mAP under the multi-shot setting of the indoor-search protocol,surpassing the best-performing method by 7.2%.展开更多
基金Supported by Science and Technology Department Major Innovation Special Fund of Hubei Province of China(2020BAB116)。
文摘With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image sets perform well in monocular 3D face reconstruction tasks,they tend to rely on the constraints of the a priori model or the appearance conditions of the input images,fundamentally because of the inability to propose an effective method to reduce the effects of two-dimensional(2D)ambiguity.To solve this problem,we developed an unsupervised training framework for monocular face 3D reconstruction using rotational cycle consistency.Specifically,to learn more accurate facial information,we first used an autoencoder to factor the input images and applied these factors to generate normalized frontal views.We then proceeded through a differentiable renderer to use rotational consistency to continuously perceive refinement.Our method provided implicit multi-view consistency constraints on the pose and depth information estimation of the input face,and the performance was accurate and robust in the presence of large variations in expression and pose.In the benchmark tests,our method performed more stably and realistically than other methods that used 3D face reconstruction in monocular 2D images.
基金supported by the National Key Research and Development Program of China in the 14th Five-Year(Nos.2021YFFO602103 and 2021YFF0602102).
文摘RGB-Infrared person re-IDentification(re-ID)aims to match RGB and infrared(IR)images of the same person.However,the modality discrepancy between RGB and IR images poses a significant challenge for re-ID.To address this issue,this paper proposes a Proxy-based Embedding Alignment(PEA)method to align the RGB and IR modalities in the embedding space.PEA introduces modality-specific identity proxies and leverages the sample-to-proxy relations to learn the model.Specifically,PEA focuses on three types of alignments:intra-modality alignment,inter-modality alignment,and cycle alignment.Intra-modality alignment aims to align sample features and proxies of the same identity within a modality.Inter-modality alignment aims to align sample features and proxies of the same identity across different modalities.Cycle alignment requires that a proxy is aligned with itself after tracing it along a cross-modality cycle(e.g.,IR→RGB→IR).By integrating these alignments into the training process,PEA effectively mitigates the impact of modality discrepancy and learns discriminative features across modalities.We conduct extensive experiments on several RGB-IR re-ID datasets,and the results show that PEA outperforms current state-of-the-art methods.Notably,on SYSU-MM01 dataset,PEA achieves 71.0%mAP under the multi-shot setting of the indoor-search protocol,surpassing the best-performing method by 7.2%.
