This paper proposes a novel approach to adding and editing clothing and movement of 4D volumetric video data in a web-based environment. While significant advancements have been made in 3D modeling and animation, efficiently editing 3D mesh data produced in sequence remains a challenging problem. Since 3D mesh data synthesized from multiple cameras exists continuously over time, modifying a single 3D mesh model requires consistent editing across multiple frames. Most existing methods focus on single meshes or static 3D models, limiting their ability to handle the complexity of time-varying 3D mesh sequences. The method proposed in this paper targets 3D volumetric sequences synthesized from multiple cameras. It utilizes deep learning networks to estimate body poses, facial features, and hand shapes from RGB images, generating 3D models using the SMPL-X method. Subsequently, an algorithm is applied to segment the 3D mesh, separating and combining the head and torso of the model to create a new 3D model. In the web-based environment, this process makes the data editable, allowing for adding new motions or replacing clothing, which can be seamlessly composited into the existing sequence video. The proposed method enables editing and modification of various types of 3D mesh sequences, facilitating enhancements to existing sequences, such as changing the motion of characters or replacing their clothing, thereby improving the overall quality of 3D content creation in online applications.

Using depth images from RGB-D cameras simplifies reconstructing 3D information for adaptive online transmission. However, depth sensors often produce distance-related distortions, leading to 3D distortions in reconstructed point clouds or meshes. This paper addresses these issues by proposing a method to enhance volumetric 3D data quality using synthesized point clouds and generating meshes with low-cost RGB-D cameras for Web 3.0 graphic services. We utilize calibration and reconstruction techniques from previous studies to create point clouds, refine them, and convert them into meshes. Finally, we improve the mesh surface using a latent diffusion model (LDM). The proposed calibration method reduced errors to 0.00926 mm in the 3D Charuco board experiment. For the Moai statue, the alignment accuracy achieved an average error of 8 mm and a standard deviation of 3.9 mm. Using LDM, the mesh surface improvement reduced the average error by 54.8% and the standard deviation by 65.9%.

In general, the importance of 6DoF (degree of freedom) 3D (dimension) volumetric contents technology is emerging in 5G (generation) telepresence service, Web-based (WebGL) graphics, computer vision, robotics, and next-generation augmented reality. Since it is possible to acquire RGB images and depth images in real-time through depth sensors that use various depth acquisition methods such as time of flight (ToF) and lidar, many changes have been made in object detection, tracking, and recognition research. In this paper, we propose a method to improve the quality of 3D models for 5G telepresence by processing images acquired through depth and RGB cameras on a multi-view camera system. In this paper, the quality is improved in two major ways. The first concerns the shape of the 3D model. A method of removing noise outside the object by applying a mask obtained from a color image and a combined filtering operation to obtain the difference in depth information between pixels inside the object were proposed. Second, we propose an illumination compensation method for images acquired through a multi-view camera system for photo-realistic 3D model generation. It is assumed that the three-dimensional volumetric shooting is done indoors, and the location and intensity of illumination according to time are constant. Since the multi-view camera uses a total of 8 pairs and converges toward the center of space, the intensity and angle of light incident on each camera are different even if the illumination is constant. Therefore, all cameras take a color correction chart and use a color optimization function to obtain a color conversion matrix that defines the relationship between the eight acquired images. Using this, the image input from all cameras is corrected based on the color correction chart. It was confirmed that the quality of the 3D model could be improved by effectively removing noise due to the proposed method when acquiring images of a 3D volumetric object using eight cameras. It has been experimentally proven that the color difference between images is reduced.

This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction, invisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques: finding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the location, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowest-frequency subbands, resulting from n-level Mallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking data and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark data into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the strength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various resolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks. With experimental results and comparison to the existing works we show that the proposed scheme has better performance than the previous works, except those which specialize in certain types of attacks.

In this paper, we proposed a new architecture of multiplier-and-accumulator (MAC) for high-speed arithmetic. By combining multiplication with accumulation and devising a hybrid type of carry save adder (CSA), the performance was improved. Since the accumulator that has the largest delay in MAC was merged into CSA, the overall performance was elevated. The proposed CSA tree uses 1's-complement-based radix-2 modified Booth's algorithm (MBA) and has the modified array for the sign extension in order to increase the bit density of the operands. The CSA propagates the carries to the least significant bits of the partial products and generates the least significant bits in advance to decrease the number of the input bits of the final adder. Also, the proposed MAC accumulates the intermediate results in the type of sum and carry bits instead of the output of the final adder, which made it possible to optimize the pipeline scheme to improve the performance. The proposed architecture was synthesized with 250, 180 and 130 ¿m, and 90 nm standard CMOS library. Based on the theoretical and experimental estimation, we analyzed the results such as the amount of hardware resources, delay, and pipelining scheme. We used Sakurai's alpha power law for the delay modeling. The proposed MAC showed the superior properties to the standard design in many ways and performance twice as much as the previous research in the similar clock frequency. We expect that the proposed MAC can be adapted to various fields requiring high performance such as the signal processing areas.

In this paper, we propose a wavelet-based audio watermarking algorithm that is robust against capturing attack. With a commercial capturing tool, it is possible to capture various audio contents without noticeable degradation, and thus can potentially facilitate the illegal distribution of the audio content. By adjusting the mean value of the lowest subband coefficients of the discrete wavelet transform (DWT) of the audio, the proposed watermark can survive capturing attack including sampling rate conversion, random cropping and compression. By incorporating a simple human auditory model, the inaudibility of the watermark achieved, and the detection probability is improved based on the difference information of extracted values. This is confirmed by experimental results