This paper presents a transformer-based framework that leverages instructional diagrams to guide 3D part assembly, addressing challenges in sequencing and pose estimation. Using contrastive learning and cross-modal attention, it aligns 2D manual steps with 3D parts, predicts assembly order, and refines poses, achieving state-of-the-art performance on PartNet and IKEA-Manual datasets. The method demonstrates strong generalization to real-world scenarios, significantly improving accuracy and robustness in automated assembly tasks. (Generated by ChatGPT4o).

This paper introduces a method for simultaneously localizing multiple instructional diagram queries in videos, addressing the limitations of current approaches that handle queries individually. The proposed method uses composite queries combining visual features and positional embeddings, reducing overlaps and correcting temporal misalignment. Tested on the IAW and YouCook2 datasets, this approach significantly improves grounding accuracy by leveraging self-attention and cross-attention mechanisms, outperforming existing methods while maintaining the temporal structure of instructional steps. (Generated by ChatGPT4o).

This paper introduces a supervised contrastive learning approach that learns to align videos with the subtle details of assembly diagrams, guided by a set of novel losses. To study this problem and evaluate the effectiveness of their method, they introduce a new dataset: IAW—for Ikea assembly in the wild—consisting of 183 hours of videos from diverse furniture assembly collections and nearly 8,300 illustrations from their associated instruction manuals and annotated for their ground truth alignments. They define two tasks on this dataset: First, nearest neighbor retrieval between video segments and illustrations, and, second, alignment of instruction steps and the segments for each video. Extensive experiments on IAW demonstrate superior performance of their approach against alternatives. (Generated by New Bing).