Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.21865

From Sketch to Scene: Relightable Garment Reconstruction from Marked Sketches

Ahmet Neçirvan Doğan·Eren Acar Başaran·Metin Sezgin·Zahra Montazeri
View original source ↗Read source document ↗Export metadata ↓

Abstract

Sketching is one of the most natural ways for people to express visual ideas. Unlike the text prompts that most generative systems rely on today, a sketch captures spatial structure and intent directly, including where a designer wants highlights and shadows to appear. Yet current sketch-to-3D pipelines have no input for that intent: they bake the drawn shading into the texture as dark paint, and the light cannot be moved. We propose a system that transforms a rough garment sketch, marked with sparse annotations (a star for a highlight and a moon for a shadow), and a short text prompt describing the texture, into a textured 3D mesh with an explicit directional light, as a scene that can be relit and edited after generation. The two symbols are the most frequent proposals in an elicitation study with art students. From the marked sketch the system predicts the scene's directional light and, separately, a displacement field that bends the surface so that the moon-marked regions fall into shadow under that light, rather than being painted dark in the texture.

Explore related subjects

cs.GRcs.CVcs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ahmet Neçirvan Doğan, Eren Acar Başaran, Metin Sezgin, Zahra Montazeri. 2026-10-15. From Sketch to Scene: Relightable Garment Reconstruction from Marked Sketches. https://arxiv.org/abs/2610.21865

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model

While text-to-motion generation is mainstream, integrating explicit spatial constraints has become a standard solution for precise animation. Existing state-of-the-art models are tailored for professional workflows, effectively processing high-quality, pre-aligned 3D inputs via unified conditioning mechanisms. However, this strictly aligned setting restricts accessibility for general users who rely on unaligned, in-the-wild 2D reference images. This accessible scenario introduces a fundamental conflict: heterogeneous geometric constraints, where user-defined global trajectories clash with local image-derived poses. To resolve this coordinate mismatch, we propose TPMDM, a diffusion model featuring a decoupled parallel control strategy that explicitly isolates and aligns these divergent signals. Furthermore, we introduce IKMo, a holistic framework that synergizes TPMDM with a VLM-powered multi-agent system. By bridging the semantic-geometric gap, IKMo translates user-friendly inputs into explicit motion constraints to drive the diffusion backbone. Experiments on HumanML3D and KIT-ML demonstrate that our method establishes a new state-of-the-art in the task of generating motions under heterogeneous trajectory and keyframe pose constraints.

cs.GR↗
arXiv2026

ResStreamGS: Residual-Sensitive Dual-Stream Gaussian Splatting for Mobile Volumetric Video Streaming

Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and computation remains challenging. Recent 3D Gaussian Splatting (3DGS) systems, such as V$^3$, enable efficient mobile streaming by encoding time-varying Gaussian attributes with commodity video codecs and hardware decoding. However, existing pipelines treat all Gaussians as frame-varying signals, which is poorly suited to full-scene content where large portions remain temporally stable. Our empirical study shows that frame-wise adaptation and video coding of all Gaussians cause quality degradation in complex full-scene content, particularly in temporally stable regions. To address this issue, we propose ResStreamGS, a residual-sensitive dual-stream framework that processes Gaussians according to their temporal behavior. ResStreamGS identifies residual-sensitive primitives through lightweight probe adaptation and reuses stable Gaussians within each Group of Pictures (GOP). Stable Gaussians are transmitted once per GOP, while only residual-sensitive Gaussians remain in the frame-wise hardware-decodable video stream. Experiments on complex full-scene volumetric videos show that ResStreamGS improves post-codec PSNR by 7.51--8.35~dB over a controlled V$^3$-style all-Gaussians baseline, reduces amortized payload by up to 56.5%, and achieves 30.883~FPS on a Qualcomm Snapdragon 778G platform.

cs.GR↗
arXiv2026

Geometric Queries on Tessellation-Free Displaced Surfaces

Displacement maps encode fine geometric detail in compact textures and simplify surface authoring, but using them typically requires pre-tessellating surfaces into dense triangle meshes, incurring a heavy memory cost. Recent tessellation-free methods enable direct ray tracing. However, broader geometric queries such as surface sampling, specular manifold search, and closest-point queries remain less explored. Supporting these operations requires surface enclosures, area estimates, and normal bounds, which scalar height intervals alone do not fully provide. We present a first-order texture-space hierarchy that unlocks these queries directly without pre-tessellation. Each node stores a local affine height plane and a conservative residual bound, retaining spatial height variation that scalar minimum--maximum bounds discard. The plane and residual provide surface enclosures for intersection and distance searches, while the plane's height and slopes supply area estimates for sampling. Additional slope bounds support specular light-path search. Building on this representation, we develop query-specific traversal, pruning, and sampling algorithms, and demonstrate them in caustics rendering, sampling displaced emitters, and surface partial differential equation (PDE) solvers. We demonstrate memory savings over pre-tessellation and performance improvements over query-specific baselines, while retaining efficient ray tracing.

cs.GR↗