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Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering
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3D sketches are widely used for visually representing the 3D shape and structure of objects or scenes. However, the creation of 3D sketch often requires users to possess professional artistic skills. Existing research efforts primarily focus on enhancing the ability of interactive sketch generation in 3D virtual systems. In this work, we propose Diff3DS, a novel differentiable rendering framework for generating view-consistent 3D sketch by optimizing 3D parametric curves under various supervisions. Specifically, we perform perspective projection to render the 3D rational B\'ezier curves into 2D curves, which are subsequently converted to a 2D raster image via our customized differentiable rasterizer. Our framework bridges the domains of 3D sketch and raster image, achieving end-toend optimization of 3D sketch through gradients computed in the 2D image domain. Our Diff3DS can enable a series of novel 3D sketch generation tasks, including textto-3D sketch and image-to-3D sketch, supported by the popular distillation-based supervision, such as Score Distillation Sampling (SDS). Extensive experiments have yielded promising results and demonstrated the potential of our framework. Project page is at https://yiboz2001.github.io/Diff3DS/.
Forward citations
Cited by 4 Pith papers
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Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction
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Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility
Dream3DVG couples a 3D Gaussian Splatting branch with a 3D vector-graphics branch to generate text-driven sketches and icons that stay consistent across views and cull occluded strokes.
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ViewCraft3D: High-Fidelity and View-Consistent 3D Vector Graphics Synthesis
A two-stage method that fits 3D Bézier curves to a reconstructed mesh and refines them with a 3D diffusion prior, producing view-consistent 3D vector graphics from a single image in about 30 minutes.
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LLM-to-Phy3D: Physically Conform Online 3D Object Generation with LLMs
An iterative prompt-refinement wrapper around LLM-to-3D generation, using CFD drag, vision-language domain scores, and visual novelty, reports 4.5% to 106.7% DPAR gains over non-refined baselines in car design.
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