45 research outputs found
SENS: Sketch-based Implicit Neural Shape Modeling
We present SENS, a novel method for generating and editing 3D models from
hand-drawn sketches, including those of an abstract nature. Our method allows
users to quickly and easily sketch a shape, and then maps the sketch into the
latent space of a part-aware neural implicit shape architecture. SENS analyzes
the sketch and encodes its parts into ViT patch encoding, then feeds them into
a transformer decoder that converts them to shape embeddings, suitable for
editing 3D neural implicit shapes. SENS not only provides intuitive
sketch-based generation and editing, but also excels in capturing the intent of
the user's sketch to generate a variety of novel and expressive 3D shapes, even
from abstract sketches. We demonstrate the effectiveness of our model compared
to the state-of-the-art using objective metric evaluation criteria and a
decisive user study, both indicating strong performance on sketches with a
medium level of abstraction. Furthermore, we showcase its intuitive
sketch-based shape editing capabilities.Comment: 18 pages, 18 figure
Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning
As 3D models become critical in today's manufacturing and product design,
conventional 3D modeling approaches based on Computer-Aided Design (CAD) are
labor-intensive, time-consuming, and have high demands on the creators. This
work aims to introduce an alternative approach to 3D modeling by utilizing
free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+,
which is a deep-learning algorithm that takes the input of a single free-hand
sketch and produces a complete and high-fidelity model that matches the sketch
input. The neural network has view- and structural-awareness enabled by a Shape
Discriminator (SD) and a Stroke Enhancement Module (SEM), which overcomes the
limitations of sparsity and ambiguity of the sketches. The network design also
brings high robustness to partial sketch input in industrial applications.Our
approach has undergone extensive experiments, demonstrating its
state-of-the-art (SOTA) performance on both synthetic and real-world datasets.
These results validate the effectiveness and superiority of our method compared
to existing techniques. We have demonstrated the conversion of free-hand
sketches into physical 3D objects using additive manufacturing. We believe that
our approach has the potential to accelerate product design and democratize
customized manufacturing
DifferSketching: How Differently Do People Sketch 3D Objects?
Multiple sketch datasets have been proposed to understand how people draw 3D
objects. However, such datasets are often of small scale and cover a small set
of objects or categories. In addition, these datasets contain freehand sketches
mostly from expert users, making it difficult to compare the drawings by expert
and novice users, while such comparisons are critical in informing more
effective sketch-based interfaces for either user groups. These observations
motivate us to analyze how differently people with and without adequate drawing
skills sketch 3D objects. We invited 70 novice users and 38 expert users to
sketch 136 3D objects, which were presented as 362 images rendered from
multiple views. This leads to a new dataset of 3,620 freehand multi-view
sketches, which are registered with their corresponding 3D objects under
certain views. Our dataset is an order of magnitude larger than the existing
datasets. We analyze the collected data at three levels, i.e., sketch-level,
stroke-level, and pixel-level, under both spatial and temporal characteristics,
and within and across groups of creators. We found that the drawings by
professionals and novices show significant differences at stroke-level, both
intrinsically and extrinsically. We demonstrate the usefulness of our dataset
in two applications: (i) freehand-style sketch synthesis, and (ii) posing it as
a potential benchmark for sketch-based 3D reconstruction. Our dataset and code
are available at https://chufengxiao.github.io/DifferSketching/.Comment: SIGGRAPH Asia 2022 (Journal Track
Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation
Significant progress has recently been made in creative applications of large
pre-trained models for downstream tasks in 3D vision, such as text-to-shape
generation. This motivates our investigation of how these pre-trained models
can be used effectively to generate 3D shapes from sketches, which has largely
remained an open challenge due to the limited sketch-shape paired datasets and
the varying level of abstraction in the sketches. We discover that conditioning
a 3D generative model on the features (obtained from a frozen large pre-trained
vision model) of synthetic renderings during training enables us to effectively
generate 3D shapes from sketches at inference time. This suggests that the
large pre-trained vision model features carry semantic signals that are
resilient to domain shifts, i.e., allowing us to use only RGB renderings, but
generalizing to sketches at inference time. We conduct a comprehensive set of
experiments investigating different design factors and demonstrate the
effectiveness of our straightforward approach for generation of multiple 3D
shapes per each input sketch regardless of their level of abstraction without
requiring any paired datasets during training
Sketch2Pose : estimating a 3D character pose from a bitmap sketch
Artists frequently capture character poses via raster sketches, then use these drawings as a reference while posing a 3D character in a specialized 3D software --- a time-consuming process, requiring specialized 3D training and mental effort. We tackle this challenge by proposing the first system for automatically inferring a 3D character pose from a single bitmap sketch, producing poses consistent with viewer expectations. Algorithmically interpreting bitmap sketches is challenging, as they contain significantly distorted proportions and foreshortening. We address this by predicting three key elements of a drawing, necessary to disambiguate the drawn poses: 2D bone tangents, self-contacts, and bone foreshortening. These elements are then leveraged in an optimization inferring the 3D character pose consistent with the artist's intent. Our optimization balances cues derived from artistic literature and perception research to compensate for distorted character proportions. We demonstrate a gallery of results on sketches of numerous styles. We validate our method via numerical evaluations, user studies, and comparisons to manually posed characters and previous work
Automatic unpaired shape deformation transfer
Transferring deformation from a source shape to a target shape is a very useful technique in computer graphics. State-of-the-art deformation transfer methods require either point-wise correspondences between source and target shapes, or pairs of deformed source and target shapes with corresponding deformations. However, in most cases, such correspondences are not available and cannot be reliably established using an automatic algorithm. Therefore, substantial user effort is needed to label the correspondences or to obtain and specify such shape sets. In this work, we propose a novel approach to automatic deformation transfer between two unpaired shape sets without correspondences. 3D deformation is represented in a high-dimensional space. To obtain a more compact and effective representation, two convolutional variational autoencoders are learned to encode source and target shapes to their latent spaces. We exploit a Generative Adversarial Network (GAN) to map deformed source shapes to deformed target shapes, both in the latent spaces, which ensures the obtained shapes from the mapping are indistinguishable from the target shapes. This is still an under-constrained problem, so we further utilize a reverse mapping from target shapes to source shapes and incorporate cycle consistency loss, i.e. applying both mappings should reverse to the input shape. This VAE-Cycle GAN (VC-GAN) architecture is used to build a reliable mapping between shape spaces. Finally, a similarity constraint is employed to ensure the mapping is consistent with visual similarity, achieved by learning a similarity neural network that takes the embedding vectors from the source and target latent spaces and predicts the light field distance between the corresponding shapes. Experimental results show that our fully automatic method is able to obtain high-quality deformation transfer results with unpaired data sets, comparable or better than existing methods where strict correspondences are required
Mobile Wound Assessment and 3D Modeling from a Single Image
The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image
Data-driven shape analysis and processing
Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing
Embodied Interactions for Spatial Design Ideation: Symbolic, Geometric, and Tangible Approaches
Computer interfaces are evolving from mere aids for number crunching into active partners in creative processes such as art and design. This is, to a great extent, the result of mass availability of new interaction technology such as depth sensing, sensor integration in mobile devices, and increasing computational power. We are now witnessing the emergence of maker culture that can elevate art and design beyond the purview of enterprises and professionals such as trained engineers and artists. Materializing this transformation is not trivial; everyone has ideas but only a select few can bring them to reality. The challenge is the recognition and the subsequent interpretation of human actions into design intent