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%.