Most existing methods of 3D clothed human reconstruction from a single image treat the clothed human as a single object without distinguishing between cloth and human body. In this regard, we present DeClotH, which separately reconstructs 3D cloth and human body from a single image. This task remains largely unexplored due to the extreme occlusion between cloth and the human body, making it challenging to infer accurate geometries and textures. Moreover, while recent 3D human reconstruction methods have achieved impressive results using text-to-image diffusion models, directly applying such an approach to this problem often leads to incorrect guidance, particularly in reconstructing 3D cloth. To address these challenges, we propose two core designs in our framework. First, to alleviate the occlusion issue, we leverage 3D template models of cloth and human body as regularizations, which provide strong geometric priors to prevent erroneous reconstruction by the occlusion. Second, we introduce a cloth diffusion model specifically designed to provide contextual information about cloth appearance, thereby enhancing the reconstruction of 3D cloth. Qualitative and quantitative experiments demonstrate that our proposed approach is highly effective in reconstructing both 3D cloth and the human body.

We present ExBluRF, a novel view synthesis method for extreme motion blurred images based on efficient radiance fields optimization. Our approach consists of two main components: 6-DOF camera trajectory-based motion blur formulation and voxel-based radiance fields. From extremely blurred images, we optimize the sharp radiance fields by jointly estimating the camera trajectories that generate the blurry images. In training, multiple rays along the camera trajectory are accumulated to reconstruct single blurry color, which is equivalent to the physical motion blur operation. We minimize the photo-consistency loss on blurred image space and obtain the sharp radiance fields with camera trajectories that explain the blur of all images. The joint optimization on the blurred image space demands painfully increasing computation and resources proportional to the blur size. Our method solves this problem by replacing the MLP-based framework to low-dimensional 6-DOF camera poses and voxel-based radiance fields. Compared with the existing works, our approach restores much sharper 3D scenes from challenging motion blurred views with the order of 10× less training time and GPU memory consumption.

There has shown a significant interest in a high performance of, at the same time, a compact size and low cost of, 3D sensor, in reflection of a growing need of 3D environmental sensing for service robotics. One of the important requirements associated with such a 3D sensor is that sensing does not irritate or disturb human in any way while working in close and continuous contact with human. Furthermore, such a 3D sensor should be reliable and robust to the change of environmental illumination as service robots are required to work day and night. This paper presents a 3D IR camera with variable structured light that is human friendly and robust enough for application to home service robots. Infrared is chosen as the sensing medium in order to meet the requirement of human friendliness and robustness to illumination change. A Digital Mirror Device (DMD) is employed to generate and project variable patterns at a high speed for real-time operation. In implementation, we emphasize the integration of modular components to support real-time sensing and compactness in size. A number of real-world experimentations are conducted, including a human face, a statue, and a plastic model. The experimental results have demonstrated that the implemented 3D IR Camera is robust to illumination change, in addition to its advantage of human friendliness.