The availability of affordable 3D full body reconstruction systems has given rise to free-viewpoint video (FVV) of human shapes. Most existing solutions produce temporally uncorrelated point clouds or meshes with unknown point vertex correspondences. Individually compressing each frame is ineffective and still yields to ultra-large data sizes. We present an end-to-end deep learning scheme to establish dense shape correspondences and subsequently compress the data. Our approach uses sparse set of "panoramic" depth maps or PDMs, each emulating an inward-viewing concentric mosaics. We then develop a learning-based technique to learn pixel-wise feature descriptors on PDMs. The results are fed into an autoencoder-based network for compression. Comprehensive experiments demonstrate our solution is robust and effective on both public and our newly captured datasets.
Different from mesh correspondence matching, animated mesh compression is a well-studied problem in computer graphics. State-of-the-art solutions however assume consecutive meshes have exact connectivity @cite_27 . @cite_8 @cite_26 conduct pre-segmentation on the mesh to ensure connectivity consistencies. PCA-based methods @cite_19 @cite_28 @cite_33 @cite_34 aim to identify different geometric clusters of human body (arms, hands, legs, torso, head, etc). Spatio-temporal analysis @cite_16 @cite_22 @cite_34 predict vertex trajectories for forming vertex groups. There is by far only a handful of works @cite_0 @cite_37 focusing directly on compressing uncorrelated mesh sequences, i.e., meshes without correspondences. The quality of these approach fall short compared with the ones with correspondences. Our learning based approach in contrast can robustly and accurately process uncorrelated mesh sequences and is able to achieve very high compression rate. Further, we employ the smoothness of the correspondence trajectory as well as geodesic consistencies for fine tuning our solution.
@cite_37 @cite_26 @cite_33 @cite_22 @cite_8 @cite_28 @cite_16 @cite_0 @cite_19 @cite_27 @cite_34
2108891011 2746892480 2118084436 2155641907
We present a new geometry compression method for animations, which is based on the clustered principal component analysis (CPCA). Instead of analyzing the set of vertices for each frame, our method analyzes the set of paths for all vertices for a certain animation length. Thus, using a data-driven approach, it can identify mesh parts, that are "coherent" over time. This usually leads to a very efficient and robust segmentation of the mesh into meaningful clusters, e.g. the wings of a chicken. These parts are then compressed separately using standard principal component analysis (PCA). Each of this clusters can be compressed more efficiently with lesser PCA components compared to previous approaches. Results show, that the new method outperforms other compression schemes like pure PCA based compression or combinations with linear prediction coding, while maintaining a better reconstruction error. This is true, even if the components and weights are quantized before transmission. The reconstruction process is very simple and can be performed directly on the GPU. While the ready availability of 3D scan data has influenced research throughout computer vision, less attention has focused on 4D data, that is 3D scans of moving non-rigid objects, captured over time. To be useful for vision research, such 4D scans need to be registered, or aligned, to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D scans of human subjects in motion, captured at 60 fps. We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology. The approach exploits consistency in texture over both short and long time intervals and deals with temporal offsets between shape and texture capture. We show how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. We evaluate the accuracy of our registration and provide a dataset of 40,000 raw and aligned meshes. Dynamic FAUST extends the popular FAUST dataset to dynamic 4D data, and is available for research purposes at http: dfaust.is.tue.mpg.de. We describe a compression method for three-dimensional animation sequences that has notable advantages over existing techniques. We first aggregate the frame data by similarity and reorganize them into clusters, which results in the sequence split into several motion fragments of varying lengths. To minimize the number of clusters and obtain optimal clustering, we perform frame alignment, which eliminates the “global” rigid transformation from each frame data and use only “pose” when evaluating the similarity between frames. We then apply principal component analysis for each cluster, from which we get coordinates of corresponding frames in a reduced dimension. Because similar frames are considered, the number of coefficients required for each frame becomes smaller; thus, we obtain better dimension reduction for a given reconstruction error. Further, we perform intracluster compression based on linear coding. Because every motion fragment presents similar frames, conventional linear predictive coding can be replaced by key frame-based linear coding to achieve minimal reconstruction error. Results show that our method can obtain a high compression ratio, with a limited reconstruction error. Copyright © 2013 John Wiley & Sons, Ltd. Time-varying mesh, which is attracting a lot of attention as a new multimedia representation method, is a sequence of 3-D models that are composed of vertices, edges, and some attribute components such as color. Among these components, vertices require large storage space. In conventional 2-D video compression algorithms, motion compensation (MC) using a block matching algorithm is frequently employed to reduce temporal redundancy between consecutive frames. However, there has been no such technology for 3-D time-varying mesh so far. Therefore, in this paper, we have developed an extended block matching algorithm (EBMA) to reduce the temporal redundancy of the geometry information in the time-varying mesh by extending the idea of the 2-D block matching algorithm to 3-D space. In our EBMA, a cubic block is used as a matching unit. MC in the 3-D space is achieved efficiently by matching the mean normal vectors calculated from partial surfaces in cubic blocks, which our experiments showed to be a suboptimal matching criterion. After MC, residuals are transformed by the discrete cosine transform, uniformly quantized, and then encoded. The extracted motion vectors are also entropy coded after differential pulse code modulation. As a result of our experiments, 10 -18 compression has been achieved.