Recovering sewing patterns from draped 3D garments is a challenging problem in human digitization research. Unlike draping from designed sewing pattern with mature physical simulation engines, the inverse process requires mapping complex garment surfaces back to parametric 2D patterns, which existing methods still struggle with. To this end, we propose a two-stage methodology that recovers sewing patterns from 3D garments via an intermediate representation, BoxMesh, which inherently encodes garment-level geometry and panel-level details in 3D space. In Stage I, a geometry-oriented auto-regressive model predicts BoxMesh from the input 3D garment. In Stage II, a semantic-aware auto-regressive model parses BoxMesh into parametric sewing patterns. This decomposition separately tackles geometric inversion and numerical reasoning, enabling more accurate recovery. Experiments have demonstrated that our method not only achieves state-of-the-art performance on the GarmentCodeData benchmark but also can be applied effectively to real scans and single-view images.

Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars, real-time performance has mostly been demon-strated for static scenes only. To address this, we propose ASH, an animatable Gaussian splatting approach for photorealistic rendering of dynamic humans in real time. We parameterize the clothed human as animatable 3D Gaussians, which can be efficiently splatted into image space to generate the final rendering. However, naively learning the Gaussian parameters in 3D space poses a severe challenge in terms of compute. Instead, we attach the Gaussians onto a deformable character model, and learn their parameters in 2D texture space, which allows leveraging efficient 2D convolutional architectures that easily scale with the required number of Gaussians. We benchmark ASH with competing methods on pose-controllable avatars, demonstrating that our method outperforms existing real-time methods by a large margin and shows comparable or even better results than offline methods.