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• W3134996813 abstract "In addition to 3D geometry, accurate representation of texture is important when digitizing real objects in virtual worlds. Based on a single consumer RGBD sensor, accurate texture representation for static objects can be realized by fusing multi-frame information; however, extending the process to dynamic objects, which typically have time-varying textures, is difficult. Thus, to address this problem, we propose a compact keyframe-based representation that decouples a dynamic texture into a basic static texture and a set of multiplicative changing maps. With this representation, the proposed method first aligns textures recorded from multiple keyframes with the reconstructed dynamic geometry of the object. Errors in the alignment and geometry are then compensated in an innovative iterative linear optimization framework. With the reconstructed texture, we then employ a scheme to synthesize the dynamic object from arbitrary viewpoints. By considering temporal and local pose similarities jointly, dynamic textures in all keyframes are fused to guarantee high-quality image generation. Experimental results demonstrate that the proposed method handles various dynamic objects, including faces, bodies, cloth, and toys. In addition, qualitative and quantitative comparisons demonstrate that the proposed method outperforms state-of-the-art solutions." @default.
• W3134996813 created "2021-03-15" @default.
• W3134996813 creator A5033696206 @default.
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• W3134996813 date "2022-10-01" @default.
• W3134996813 modified "2023-10-09" @default.
• W3134996813 title "DTexFusion: Dynamic Texture Fusion Using a Consumer RGBD Sensor" @default.
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• W3134996813 doi "https://doi.org/10.1109/tvcg.2021.3064846" @default.
• W3134996813 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33687843" @default.
• W3134996813 hasPublicationYear "2022" @default.
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