Abstract Hand‐drawn model sheets are widely used in character design to define 3D shape and appearance through sparse multi‐view drawings. Reconstructing 3D characters from such sparse inputs has traditionally been challenging due to insufficient visual information. Recently, 3D generative models have enabled automatic reconstruction of plausible 3D characters by learning from large‐scale training data, but achieving reconstructions that accurately match the input model sheets remains limited. In this paper, we present a framework that leverages the power of a 3D generative model for initial reconstruction and enhances the output to faithfully reproduce input model sheets. For such faithful reconstruction, we must address two fundamental challenges: (1) the hand‐drawn nature inherently introduces multi‐view inconsistencies where the generated 3D geometry cannot perfectly align with all views, and (2) view‐dependent line elements along geometry boundaries interfere with accurate texture reconstruction. To address these challenges, we optimize the geometry to minimize multi‐view inconsistencies and introduce a deformable per‐pixel camera ray representation that resolves residual discrepancies in cross‐view correspondences. We also decompose drawings into three distinct layers of view‐dependent lines, view‐independent colors, and fine‐detail decals to separately handle view‐dependent and view‐independent components for consistent cross‐view reconstruction. Comprehensive experiments demonstrate that our method outperforms possible alternatives regardless of the choice of 3D generative model, while successfully preserving both artistic intent and visual fidelity of input model sheets.

Self-intersection repair in static 3D surface meshes presents unique challenges due to the absence of temporal motion and penetration depth information—two critical elements typically leveraged in physics-based approaches. We introduce a novel framework that transforms local contact handling into a global repair strategy through a combination of local signed tangent-point energies and their gradient diffusion. At the heart of our method is a key insight: rather than computing expensive global repulsive potentials, we can effectively approximate long-range interactions by diffusing energy gradients from local contacts throughout the mesh surface. In turn, resolving complex self-intersections reduces to simply propagating local repulsive energies through standard diffusion mechanics and iteratively solving tractable local optimizations. We further accelerate convergence through our momentum-based optimizer, which adaptively regulates momentum based on gradient statistics to prevent overshooting while maintaining rapid intersection repair. The resulting algorithm handles a variety of challenging scenarios, from shallow contacts to deep penetrations, while providing computational efficiency suitable for interactive applications.

We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN , automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. The key component of our method is shape exaggeration blocks that are used for modulating coarse layer feature maps of StyleGAN to produce desirable caricature shape exaggerations. We first build a layer-mixed StyleGAN for photo-to-caricature style conversion by swapping fine layers of the StyleGAN for photos to the corresponding layers of the StyleGAN trained to generate caricatures. Given an input photo, the layer-mixed model produces detailed color stylization for a caricature but without shape exaggerations. We then append shape exaggeration blocks to the coarse layers of the layer-mixed model and train the blocks to create shape exaggerations while preserving the characteristic appearances of the input. Experimental results show that our StyleCariGAN generates realistic and detailed caricatures compared to the current state-of-the-art methods. We demonstrate StyleCariGAN also supports other StyleGAN-based image manipulations, such as facial expression control.