. Garment design is complex, given the requirement to satisfy multiple specifications including target sketched fold pa erns, 3D body shape, or 2D sewing pa erns and/or textures. We learn a novel shared shape space spanning di erent input modalities that allows the designer to seamlessly work across the multiple domains to interactively design and edit garments without requiring access to expensive physical simulations at design time, and retarget the design to a range of body shapes. Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realistic dressed digital humans for various applications. is necessitates creating simple and e ective work ows to facilitate authoring sewing patterns customized to garment and target body shapes to achieve desired looks. Traditional work ow involves a trial-and-error procedure wherein a mannequin is draped to judge the resultant folds and the sewing pa ern iteratively adjusted until the desired look is achieved. is requires time and experience. Instead, we present a data-driven approach wherein the user directly indicates desired fold pa erns simply by sketching while our system estimates corresponding garment and body shape parameters at interactive rates. e recovered parameters can then be further edited and the updated draped garment previewed. Technically, we achieve this via a novel shared shape space that allows the user to seamlessly specify desired characteristics across multimodal input without requiring to run garment simulation at design time. We evaluate our approach qualitatively via a user study and quantitatively against test datasets, and demonstrate how our system can generate a rich quality of on-body garments targeted for a range of body shapes while achieving desired fold characteristics. Additional Key Words and Phrases: garment design, shared shape space, latent representation, multimodal input, interactive design ACM Reference format:
Recent work incorporates advancements in machine learning to develop a data-driven approach, which uses a sketch of desired fold patterns to estimate corresponding garment shapes via a novel shared shape-space REF .
Learning a Shared Shape Space for Multimodal Garment Design
SIGGRAPH Asia '18
Computer Science Mathematics