# Wired Perspectives: Multi-View Wire Art Embraces Generative AI

arXiv: https://arxiv.org/abs/2311.15421 · HTML: https://mlsanity.com/paper/2311.15421/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 26 Nov 2023.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Modular Primitives for High-Performance Differentiable Rendering](https://mlsanity.com/paper/2011.03277/) (2020)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior](https://mlsanity.com/paper/2303.14184/) (2023)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [Diffusion models as plug-and-play priors](https://mlsanity.com/paper/2206.09012/) (2022)
- [CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders](https://mlsanity.com/paper/2106.14843/) (2021)
- [VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models](https://mlsanity.com/paper/2211.11319/) (2022)
- [DemoFusion: Democratising High-Resolution Image Generation With No ](https://mlsanity.com/paper/2311.16973/) (2023)
- [Improving Image Captioning with Better Use of Captions](https://mlsanity.com/paper/2006.11807/) (2020)
- [CLIPasso: Semantically-Aware Object Sketching](https://mlsanity.com/paper/2202.05822/) (2022)
- [HeadSculpt: Crafting 3D Head Avatars with Text](https://mlsanity.com/paper/2306.03038/) (2023)
- [B'ezierSketch: A generative model for scalable vector sketches](https://mlsanity.com/paper/2007.02190/) (2020)
- [StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation](https://mlsanity.com/paper/2202.12362/) (2022)
- [SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation](https://mlsanity.com/paper/2308.14191/) (2023)
- [Constructing Printable Surfaces with View-Dependent Appearance](https://mlsanity.com/paper/2306.07449/) (2023)

## Built on by (most cited first, 17 of 17)

- [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024)
- [PartCraft: Crafting Creative Objects by Parts](https://mlsanity.com/paper/2407.04604/) (2024)
- [Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering](https://mlsanity.com/paper/2405.15305/) (2024)
- [FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations](https://mlsanity.com/paper/2411.10818/) (2024)
- [Illusion3D: 3D Multiview Illusion with 2D Diffusion Priors](https://mlsanity.com/paper/2412.09625/) (2024)
- [ViewCraft3D: High-Fidelity and View-Consistent 3D Vector Graphics Synthesis](https://mlsanity.com/paper/2505.19492/) (2025)
- [WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction](https://mlsanity.com/paper/2505.04813/) (2025)
- [Hand-Shadow Poser](https://mlsanity.com/paper/2505.07012/) (2025)
- [Neural Image Abstraction Using Long Smoothing B-Splines](https://mlsanity.com/paper/2511.05360/) (2025)
- [Neural Shadow Art](https://mlsanity.com/paper/2411.19161/) (2024)
- [Recovering Dynamic 3D Sketches from Videos](https://mlsanity.com/paper/2503.20321/) (2025)
- [Shape from Semantics: 3D Shape Generation from Multi-View Semantics](https://mlsanity.com/paper/2502.00360/) (2025)
- [ShadowDraw: From Any Object to Shadow-Drawing Compositional Art](https://mlsanity.com/paper/2512.05110/) (2025)
- [Optimizing 4D Wires for Sparse 3D Abstraction](https://mlsanity.com/paper/2605.11977/) (2026)
- [JanusMesh: Fast and Zero-Shot 3D Visual Illusion Generation via Cross-Space Denoising](https://mlsanity.com/paper/2606.20563/) (2026)
- [Mirror Illusion Art](https://mlsanity.com/paper/2607.02015/) (2026)
- [Windfoil: Closed-Form Coverage for Real-Time and Differentiable Vector Graphics](https://mlsanity.com/paper/2610.02468/) (2026)

## Abstract (arXiv)

Creating multi-view wire art (MVWA), a static 3D sculpture with diverse interpretations from different viewpoints, is a complex task even for skilled artists. In response, we present DreamWire, an AI system enabling everyone to craft MVWA easily. Users express their vision through text prompts or scribbles, freeing them from intricate 3D wire organisation. Our approach synergises 3D B'ezier curves, Prim's algorithm, and knowledge distillation from diffusion models or their variants (e.g., ControlNet). This blend enables the system to represent 3D wire art, ensuring spatial continuity and overcoming data scarcity. Extensive evaluation and analysis are conducted to shed insight on the inner workings of the proposed system, including the trade-off between connectivity and visual aesthetics.