mlsanityData to 14 Oct 2026

Sketch2Stress: Sketching with Structural Stress Awareness

arXiv 2306.05911 ↗First posted 9 Jun 2023cs.CV cs.GRcited by 1

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Abstract (arXiv)

In the process of product design and digital fabrication, the structural analysis of a designed prototype is a fundamental and essential step. However, such a step is usually invisible or inaccessible to designers at the early sketching phase. This limits the user's ability to consider a shape's physical properties and structural soundness. To bridge this gap, we introduce a novel approach Sketch2Stress that allows users to perform structural analysis of desired objects at the sketching stage. This method takes as input a 2D freehand sketch and one or multiple locations of user-assigned external forces. With the specially-designed two-branch generative-adversarial framework, it automatically predicts a normal map and a corresponding structural stress map distributed over the user-sketched underlying object. In this way, our method empowers designers to easily examine the stress sustained everywhere and identify potential problematic regions of their sketched object. Furthermore, combined with the predicted normal map, users are able to conduct a region-wise structural analysis efficiently by aggregating the stress effects of multiple forces in the same direction. Finally, we demonstrate the effectiveness and practicality of our system with extensive experiments and user studies.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 58 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

18 of 58 references.

References · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  7. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  8. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  9. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  10. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
  11. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
  12. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
6 more
  1. 2021Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketchescited by 54
  2. 2021Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketchescited by 51
  3. 20173D Shape Reconstruction from Sketches via Multi-view Convolutional Networkscited by 45
  4. 2018Learning 3D Shape Completion under Weak Supervisioncited by 40
  5. 20173D Sketching using Multi-View Deep Volumetric Predictioncited by 37
  6. 2018Lightweight Structure Design Under Force Location Uncertaintycited by 1

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Citing papers per year: 2026: 1.

1 paper.