We propose a robust and highly realistic clothing modeling method to generate a 3D clothing model with visually consistent clothing style and wrinkles distribution from a single RGB image. Notably, this entire process only takes a few seconds. Our high-quality clothing results benefit from the idea of combining learning and optimization, making it highly robust. First, we use the neural networks to predict the normal map, a clothing mask, and a learning-based clothing model from input images. The predicted normal map can effectively capture high-frequency clothing deformation from image observations. Then, by introducing a normal-guided clothing fitting optimization, the normal maps are used to guide the clothing model to generate realistic wrinkles details. Finally, we utilize a clothing collar adjustment strategy to stylize clothing results using predicted clothing masks. An extended multi-view version of the clothing fitting is naturally developed, which can further improve the realism of the clothing without tedious effort. Extensive experiments have proven that our method achieves state-of-the-art clothing geometric accuracy and visual realism. More importantly, it is highly adaptable and robust to in-the-wild images. Further, our method can be easily extended to multi-view inputs to improve realism. In summary, our method can provide a low-cost and user-friendly solution to achieve realistic clothing modeling.

Sketch-based modeling studies reconstructing models from sketches automatically, allowing users visualize design concepts rapidly. Generating CAD models based on user sketches helps reduce the learning curve for novice users, which promotes the everyday use of CAD software, and expands its reach to non-professional groups. While various algorithms study automatically generating models from single sketch or line drawing, they often produce non-editable models or editable models limited to simple extrusion operations. To improve this issue, we propose a novel sketch-based modeling system, Sketch2Seq, which generates complex, semantic, and editable CAD models. Our system eliminates the need for additional annotations from users and produces models that support subsequent application in commercial software. The core of our method lies in understanding users' design intent from CAD sketches. We design a novel sketch segmentation network for identifying diverse operation features in CAD sketches, which utilizes geometric features of strokes and different levels of topological connections. Additionally, to tackle the segmentation task, a dataset for CAD sketch segmentation is introduced. Comparative experiments and ablation evaluations prove the effectiveness of the proposed method. Based on segmentation result, coarse CAD sequences are generated and progressively executed. Meanwhile, the orders and parameters of the CAD sequences are optimized with context models and input sketches. All algorithms are integrated into a user interface. Experiments and evaluations validate the feasibility and superiority of our entire system which is able to reconstruct more complex features and achieve better results for longer sequence.

How to estimate camera pose from motion-blurred images remains a challenge for visual odometry. The blurring artifacts are inevitably caused by the exposure during camera motion. While current visual odometry regards them as noise, we argue that it is necessary to extract potential information from blur artifacts, as they contain prior knowledge of camera motion. Base on this, we propose a blur-robust visual odometry that improves the accuracy of camera pose estimation through exposure trajectory. Specifically, we first use the exposure trajectory to guide pixel matching between neighboring frames. The blur mask is then generated based on the magnitude of the exposure trajectory. The mask makes the pose module pay less attention to the feature information in the severely blurred regions. Experiments show that our proposed end-to-end visual odometry achieves competitive performance on most sequences of motion blurred datasets.

Single-view clothing reconstruction usually relies on topologically fixed clothing templates to reduce the problem complexity, but this strategy also makes the reconstructed clothing shape contours simple and lack diversity. In this article, we propose a novel clothing reconstruction method to generate complex shape contours and open clothing mesh from a single image. At the heart of our work is an implicit unsigned distance field condition on clothing-oriented and pose-stable spatial shape features to represent the clothing from the image. This feature can provide spatially aligned clothing shape priors to improve the pose robustness. It is based on a type-generic clothing template derived from the mainstream clothing generative model to avoid tedious template design and switching. To output open clothing mesh results from noisy clothing unsigned distance fields, we develop a two-stage clothing mesh extraction method. It takes the point clouds as an intermediate representation and produces smooth, plausible and editable clothing mesh results. To provide effective supervision, we construct a pose-rich and shape-complete clothing scan dataset by enhancing clothing pose diversity and complementing missing clothing geometry caused by occlusion. Extensive experiments demonstrate that our method achieves state-of-the-art levels. More importantly, we provide a simple but effective, and low-cost way to reconstruct complex shape contours clothing from a single image.

High-quality 3D reconstruction results are very important in many application fields. However, current texture generation methods based on point sampling and fusion often produce blur. To solve this problem, we propose a new volumetric fusion strategy which can be embedded in the current online and offline reconstruction framework as a basic module to achieve excellent geometry and texture effects. The improvement comes from two aspects. Firstly, we establish an adaptive weight field to evaluate and adjust the reliability of data from RGB-D images by using a probabilistic and heuristic method. By using this adaptive weight field to guide the voxel fusion process, we can effectively preserve the local texture structure of the mesh, avoid wrong texture problems and suppress the influence of outlier noise on the geometric surface. Secondly, we use a new texture fusion strategy that combines replacement, integration, and fixedness operations to fuse and update voxel texture to reduce blur. Experimental results demonstrate that compared with the classical KinectFusion, our approach can significantly improve the accuracy in geometry and texture clarity, and can achieve equivalent texture reconstruction effects in real-time as the offline reconstruction methods such as intrinsic3d, even better in relief scenes.