DeepSketchHair: Deep Sketch-based 3D Hair Modeling

Shen, Yuefan; Zhang, Changgeng; Fu, Hongbo; Zhou, Kun; Zheng, Youyi

doi:10.48550/arxiv.1908.07198

Cited by 4 publications

(6 citation statements)

References 47 publications

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“…Sketch to 3D model generation is also an interesting crossmodal research topic analogous to the sketch-to-image generation discussed in Section 4.2.3. Using sketch to generate 3D models/shapes [306], [307] is extremely challenging but has important applications such as sketch-based product design [308], [309]. Compared to the other tasks discussed, this is relatively under-studied thus far.…”

Section: Sketch-3d Generationmentioning

confidence: 99%

“…DeepSketchHair [308] is a representative deep model that generates realistic 3D hairstyle models from 2D sketches. As shown in Figure 17, given a 3D bust model as a reference, the system takes in a user-drawn sketch (consisting of hair contour (red lines) and a few strokes indicating the hair growing directions (blue lines) within a hair region), and automatically generates a 3D hair model, which matches the input sketch both globally (for contour) and locally (for growing directions).…”

Section: Sketch-3d Generationmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Free-Hand Sketch: A Survey

Xu¹,

Hospedales²,

Yin³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly popular. The progress of deep learning has immensely benefited free-hand sketch research and applications. This paper presents a comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable. The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities, e.g., natural photos. (ii) A review of the developments of free-hand sketch research in the deep learning era, by surveying existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy and experimental evaluation. (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.

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Section: Sketch-3d Generationmentioning

confidence: 99%

Section: Sketch-3d Generationmentioning

confidence: 99%

Deep Learning for Free-Hand Sketch: A Survey

Xu¹,

Hospedales²,

Yin³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…Deng et al [6] proposed to extract free-form shapes from single images in an interactive way, inspired by the lofting technique in CAD systems. By utilizing deep learning techniques, several works [5,11,12,15,23,31,32,35,36] have achieved to gain priors from existing 3D models, while being able to leverage user inputs. However, very few of them focuses on reconstruction from free-hand sketches [35,36], instead of precise line drawings.…”

Section: Sketch-based 3d Modelingmentioning

confidence: 99%

Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches

Zhang

Guo

2021

Preprint

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We investigate the problem of generating 3D meshes from single free-hand sketches, aiming at fast 3D modeling for novice users. It can be regarded as a single-view reconstruction problem, but with unique challenges, brought by the variation and conciseness of sketches. Ambiguities in poorly-drawn sketches could make it hard to determine how the sketched object is posed. In this paper, we address the importance of viewpoint specification for overcoming such ambiguities, and propose a novel view-aware generation approach. By explicitly conditioning the generation process on a given viewpoint, our method can generate plausible shapes automatically with predicted viewpoints, or with specified viewpoints to help users better express their intentions. Extensive evaluations on various datasets demonstrate the effectiveness of our view-aware design in solving sketch ambiguities and improving reconstruction quality.

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“…The generation across sketch and 3D model is also an interesting cross-modal research topic. Using sketch to generate 3D models/shapes [223] is an extremely challenging and application-driven task, which has some application scenarios, e.g., sketch-based product design [224]. Due to the task-unique challenges, this task is under-studied to date.…”

Section: Sketch-3d Generationmentioning

confidence: 99%

Deep Learning for Free-Hand Sketch: A Survey

Xu¹

2020

Preprint

View full text Add to dashboard Cite

Free-hand sketches are highly hieroglyphic and illustrative, which have been widely used by humans to depict objects or stories from ancient times to the present. The recent prevalence of touchscreen devices has made sketch creation a much easier task than ever and consequently made sketch-oriented applications increasingly more popular. The prosperity of deep learning has also immensely promoted the research for the free-hand sketch. This paper presents a comprehensive survey of the free-hand sketch oriented deep learning techniques. The main contents of this survey include: (i) The intrinsic traits and domain-unique challenges of the free-hand sketch are discussed, to clarify the essential differences between free-hand sketch and other data modalities, e.g., natural photo. (ii) The development of the free-hand sketch community in the deep learning era is reviewed, by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy. (iii) Moreover, the bottlenecks, open problems, and potential research directions of this community have also been discussed to promote the future works.

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DeepSketchHair: Deep Sketch-based 3D Hair Modeling

Cited by 4 publications

References 47 publications

Deep Learning for Free-Hand Sketch: A Survey

Deep Learning for Free-Hand Sketch: A Survey

Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches

Deep Learning for Free-Hand Sketch: A Survey

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