Sketches, with their expressive potential, enable humans to convey the essence of an object through a rough contour. This work leverages expressive power for the first time to improve segmentation performance in challenging tasks such as camouflaged object detection (COD). We propose a sketch guided interactive segmentation framework that allows users to intuitively annotate objects with freehand sketches rather than relying on traditional bounding boxes or points commonly used in models such as the SAM. Our method introduces dedicated network architectural enhancements and a novel sketch augmentation strategy to fully exploit sketch input, leading to significant accuracy gains compared with text- or box-based annotations. Furthermore, our model's output can directly train other neural networks, achieving performance comparable to that of pixel-level annotations while reducing the annotation time by up to 120× and thereby lowering the barrier for large-scale dataset creation and model training. To support future research, werelease KOSCamo+, the first freehand sketch dataset for COD, along with code and a labeling tool. These contributions open promising avenues for expanding sketch-based interaction to broader segmentation tasks and exploring multimodal annotation strategies that combine sketches, text, and other lightweight user inputs.

This paper presents a novel approach for generating high-quality, cross-category 3D models from free-hand sketches with limited training data. We propose the first semi-supervised learning method to our knowledge for sketch-to-3D model conversion. Innovatively, we design a coarse-to-fine pipeline to perform the semi-supervised learning in the coarse stage and train a diffusion-based refiner to get a high-resolution 3D model. We designed a sketch-augmentation method for semi-supervised learning and integrated priors such as CLIP loss, shape prototypes, and adversarial loss to help generate high-quality results even with abstract and imprecise sketches. We also introduce an innovative procedural 3D generation method based on CAD code, which helps pre-train part of the network before fine-tuning with limited real data. Our approach, coupled with a specifically designed curriculum learning, allows us to generate high-quality 3D models across multiple categories with as few as 300 sketch-3D model pairs, marking a significant advancement over previous single-category approaches. In addition, we introduce the KO2D dataset, the largest collection of hand-drawn sketch-3D pairs to support further research in this area. As sketches are a far more intuitive and detailed way for users to express their unique ideas, we believe that this paper can move us closer to democratizing 3D content creation, enabling anyone to transform their ideas into 3D models effortlessly.

In this article, we propose Img2CAD, the first approach to our knowledge that uses 2-D image inputs to generate computer-aided design (CAD) models with editable parameters. Unlike existing artificial intelligence (AI) methods for 3-D model generation using text or image inputs often rely on mesh-based representations, which are incompatible with CAD tools and lack editability and fine control, Img2CAD enables seamless integration between AI-based 3-D reconstruction and CAD software. We have identified an innovative intermediate representation called structured visual geometry, characterized by vectorized wireframes extracted from objects. This representation significantly enhances the performance of generating conditioned CAD models. In addition, we introduce two new datasets to further support research in this area:a big cad model dataset (ABC)-mono, the largest known dataset comprising over 200 000 3-D CAD models with rendered images, andKOCAD, the first dataset featuring real-world captured objects alongside their ground truth CAD models, supporting further research in conditioned CAD model generation.

In the era of immersive consumer electronics, such as AR/VR headsets and smart devices, people increasingly seek ways to express their identity through virtual fashion. However, existing 3D garment design tools remain inaccessible to everyday users due to steep technical barriers and limited data. In this work, we introduce a 3D sketch-driven 3D garment generation framework that empowers ordinary users - even those without design experience - to create high-quality digital clothing through simple 3D sketches in AR/VR environments. By combining a conditional diffusion model, a sketch encoder trained in a shared latent space, and an adaptive curriculum learning strategy, our system interprets imprecise, free-hand input and produces realistic, personalized garments. To address the scarcity of training data, we also introduce KO3DClothes, a new dataset of paired 3D garments and user-created sketches. Extensive experiments and user studies confirm that our method significantly outperforms existing baselines in both fidelity and usability, demonstrating its promise for democratized fashion design on next-generation consumer platforms.

With the emergence of AR/VR, 3D models are in tremendous demand. However, conventional 3D modeling with Computer-Aided Design software requires much expertise and is difficult for novice users. We find that AR/VR devices, in addition to serving as effective display mediums, can offer a promising potential as an intuitive 3D model creation tool, especially with the assistance of AI generative models. Here, we propose Deep3DVRSketch, the first 3D model generation network that inputs 3D VR sketches from novice users and generates highly consistent 3D models in multiple categories within seconds, irrespective of the users' drawing abilities. We also contribute KO3D+, the largest 3D sketch-shape dataset. Our method pre-trains a conditional diffusion model on quality 3D data, then fine-tunes an encoder to map 3D sketches onto the generator's manifold using an adaptive curriculum strategy for limited ground truths. In our experiment, our approach achieves state-of-the-art performance in both model quality and fidelity with real-world input from novice users, and users can even draw and obtain very detailed geometric structures. In our user study, users were able to complete the 3D modeling tasks over 10 times faster using our approach compared to conventional CAD software tools. We believe that our Deep3DVRSketch and KO3D+ dataset can offer a promising solution for future 3D modeling in metaverse era. Check the project page at http://research.kokoni3d.com/Deep3DVRSketch.

Accurately distinguishing between action-related features and non-action-related features is crucial in spatio-temporal action detection tasks. Additionally, the calibration and fusion of information across different modalities remain challenging. This paper proposes a novel Motion Sense and Semantic Correction framework (MS-SC) to address these issues. The MS-SC framework achieves accurate detection by fusing features from images (spatial dimension) and videos (spatio-temporal dimension). A Motion Sense Module (MSM) is proposed to significantly increase the feature distance between action and non-action features in the semantic space, enhancing feature discriminability. Considering the complementary nature of information across different modalities, an efficient Semantic Correction Fusion Module (SFM) is introduced to facilitate interaction between features of distinct modalities and maximize their complementary information integration. To evaluate the performance of the MS-SC framework, extensive experiments were conducted on two challenging datasets, UCF101-24 and AVA. The results demonstrate the effectiveness of the MS-SC framework in handling spatio-temporal action detection tasks.

The rise of artificial intelligence generated content (AIGC) has been remarkable in the language and image fields, but artificial intelligence (AI) generated three-dimensional (3D) models are still under-explored due to their complex nature and lack of training data. The conventional approach of creating 3D content through computer-aided design (CAD) is labor-intensive and requires expertise, making it challenging for novice users. To address this issue, we propose a sketch-based 3D modeling approach, Deep3DSketch-im, which uses a single freehand sketch for modeling. This is a challenging task due to the sparsity and ambiguity. Deep3DSketch-im uses a novel data representation called the signed distance field (SDF) to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points, and a specially designed neural network that can capture point and local features. Extensive experiments are conducted to demonstrate the effectiveness of the approach, achieving state-of-the-art (SOTA) performance on both synthetic and real datasets. Additionally, users show more satisfaction with results generated by Deep3DSketch-im, as reported in a user study. We believe that Deep3DSketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.

Deep learning-based image classification networks heavily rely on the extracted features. However, as the model becomes deeper, important features may be lost, resulting in decreased accuracy. To tackle this issue, this paper proposes an image classification method that enhances low-level features and incorporates an attention mechanism. The proposed method employs EfficientNet as the backbone network for feature extraction. Firstly, the Feature Enhancement Module quantifies and statistically processes low-level features from shallow layers, thereby enhancing the feature information. Secondly, the Convolutional Block Attention Module enhances the high-level features to improve the extraction of global features. Finally, the enhanced low-level features and global features are fused to supplement low-resolution global features with high-resolution details, further improving the model’s image classification ability. Experimental results illustrate that the proposed method achieves a Top-1 classification accuracy of 86.49% and a Top-5 classification accuracy of 96.90% on the ETH-Food101 dataset, 86.99% and 97.24% on the VireoFood-172 dataset, and 70.99% and 92.73% on the UEC-256 dataset. These results demonstrate that the proposed method outperforms existing methods in terms of classification performance.

The emerging trend of AR/VR places great demands on 3D content. However, most existing software requires expertise and is difficult for novice users to use. In this paper, we aim to create sketch-based modeling tools for user-friendly 3D modeling. We introduce Reality3DSketch with a novel application of an immersive 3D modeling experience, in which a user can capture the surrounding scene using a monocular RGB camera and can draw a single sketch of an object in the real-time reconstructed 3D scene. A 3D object is generated and placed in the desired location, enabled by our novel neural network with the input of a single sketch. Our neural network can predict the pose of a drawing and can turn a single sketch into a 3D model with view and structural awareness, which addresses the challenge of sparse sketch input and view ambiguity. We conducted extensive experiments synthetic and real-world datasets and achieved state-of-the-art (SOTA) results in both sketch view estimation and 3D modeling performance. According to our user study, our method of performing 3D modeling in a scene is$>$5x faster than conventional methods. Users are also more satisfied with the generated 3D model than the results of existing methods.

This work aims to investigate the problem of 3D modeling using single free-hand sketches, which is one of the most natural ways we humans express ideas. Although sketch-based 3D modeling can drastically make the 3D modeling process more accessible, the sparsity and ambiguity of sketches bring significant challenges for creating high-fidelity 3D models that reflect the creators’ ideas. In this work, we propose a view-and structural-aware deep learning approach, Deep3DSketch, which tackles the ambiguity and fully uses sparse information of sketches, emphasizing the structural information. Specifically, we introduced random pose sampling on both 3D shapes and 2D silhouettes, and an adversarial training scheme with an effective progressive discriminator to facilitate learning of the shape structures. Extensive experiments demonstrated the effectiveness of our approach, which outperforms existing methods – with state-of-the-art (SOTA) performance on both synthetic and real datasets.

Designing attractive images for object display can be a time-consuming and skill-intensive process. The emergence of advanced algorithms, particularly the Diffusion Model, has made it possible to synthesize attractive images using AI. However, the existing diffusion models are mostly used to generate entire images and lack control over specific objects for object display. Here, to the best of our knowledge, we pioneers to extend the application of the diffusion model to synthesize novel images for specific objects. By encoding the input images of objects into NeRF representation and synthesizing the desired backgrounds using diffusion models with the input of rendered object images and text prompts, our method can generate 3D aware object display images at arbitrary angles and arbitrary backgrounds. We have conducted extensive experiments to demonstrate that our method is capable of generating high-quality and photo-realistic images, which are > 6 times faster than the conventional photomontage approach. Moreover, our generated images have higher compositional scores, image quality scores, and aesthetics scores in our user experiments. By significantly reducing the need for human effort and producing higher quality generated images, our approach opens up exciting possibilities for creating versatile novel images of specific objects.

The rise of AR/VR has led to an increased demand for 3D content. However, the traditional method of creating 3D content using Computer-Aided Design (CAD) is a labor-intensive and skill-demanding process, making it difficult to use for novice users. Sketch-based 3D modeling provides a promising solution by leveraging the intuitive nature of human-computer interaction. However, generating high-quality content that accurately reflects the creator's ideas can be challenging due to the sparsity and ambiguity of sketches. Furthermore, novice users often find it challenging to create accurate drawings from multiple perspectives or follow step-by-step instructions in existing methods. To address this, we introduce a groundbreaking end-to-end approach in our work, enabling 3D modeling from a single free-hand sketch, Deep3DSketch+\+. The issue of sparsity and ambiguity using single sketch is resolved in our approach by leveraging the symmetry prior and structural-aware shape discriminator. We conducted comprehensive experiments on diverse datasets, including both synthetic and real data, to validate the efficacy of our approach and demonstrate its state-of-the-art (SOTA) performance. Users are also more satisfied with results generated by our approach according to our user study. We believe our approach has the potential to revolutionize the process of 3D modeling by offering an intuitive and easy-to-use solution for novice users.

Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities’ sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The “Feature Ring” is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.