To address the error propagation problem, we identify image fusion as the key point for improvement.

To address these issues, we propose a content-aware retargeting method called PruneRepaint.

Theoretically, for any two geometric surfaces with identical/similar topological structures, there exists a bijective mapping between them. Nevertheless, when the topology of the target 3D surface becomes complicated (e.g., with high genus), one must pre-open the original mesh to a sufficiently-developable

To address the error propagation problem, we identify image fusion as the key point for improvement.

In this paper, we propose a novel, automatic tree-based evaluation metric for LLM-generated step-by-step assembly instructions, that more accurately reflects spatiotemporal aspects of construction than traditional metrics such as BLEU and BERT similarity scores. We apply our proposed metric to the domain of sewing instructions, and show that our metric better correlates with manually-annotated error counts as well as human quality ratings, demonstrating our metric's superiority for evaluating the spatiotemporal soundness of sewing instructions. Further experiments show that our metric is more robust than traditional approaches against artificially-constructed counterfactual examples that are specifically constructed to confound metrics that rely on textual similarity.

Surface cutting is a fundamental task in computer graphics, with applications in UV parameterization, texture mapping, and mesh decomposition. However, existing methods often produce technically valid but overly fragmented atlases that lack semantic coherence. We introduce SeamGPT, an auto-regressive model that generates cutting seams by mimicking professional workflows. Our key technical innovation lies in formulating surface cutting as a next token prediction task: sample point clouds on mesh vertices and edges, encode them as shape conditions, and employ a GPT-style transformer to sequentially predict seam segments with quantized 3D coordinates. Our approach achieves exceptional performance on UV unwrapping benchmarks containing both manifold and non-manifold meshes, including artist-created, and 3D-scanned models. In addition, it enhances existing 3D segmentation tools by providing clean boundaries for part decomposition.

Sewing garments using robots has consistently posed a research challenge due to the inherent complexities in fabric manipulation. In this paper, we introduce an intelligent robotic automation system designed to address this issue. By employing a patented technique that temporarily stiffens garments, we eliminate the traditional necessity for fabric modeling. Our methodological approach is rooted in a meticulously designed three-stage pipeline: first, an accurate pose estimation of the cut fabric pieces; second, a procedure to temporarily join fabric pieces; and third, a closed-loop visual servoing technique for the sewing process. Demonstrating versatility across various fabric types, our approach has been successfully validated in practical settings, notably with cotton material at the Bluewater Defense production line and denim material at Levi's research facility. The techniques described in this paper integrate robotic mechanisms with traditional sewing machines, devising a real-time sewing algorithm, and providing hands-on validation through a collaborative robot setup.

Chicago's Field Museum is home to over a dozen ancient Egyptian mummies but one in particular has perplexed researchers for years. Now, the mystery of Lady Chenet-aa's burial procedure appears to be solved with the use of a CT scanner. Lady Chenet-aa lived roughly 3,000 years ago amid the 22nd Dynasty during Egypt's Third Intermediate Period. Soon after her death, one of the ways funerary experts prepared her for the afterlife was by constructing a cartonnage--a paper mache-like box housing a deceased person's body. In Chenet-aa's case, however, there isn't any hint of a visible seam, leaving Egyptologists to wonder for years exactly how embalmers placed her inside the casing. According to an October 24 announcement from the Field Museum, a mobile CT scanner helped to finally explain the strategy behind Chenet-aa's "locked-mummy" cartonnage, as well as new physical information about her at her time of death.