Text-to-image (T2I) models demonstrate strong generative capabilities and are increasingly used in design. However, their support for early exploratory ideation remains limited. Their linear, one-shot interaction paradigm aligns more closely with convergent, refinement-oriented stages of design. To address this gap, we present an interaction paradigm supporting early-stage ideation with T2I models, with a particular focus on novice designers. It introduces a dimension–attribute dictionary to guide prompt construction progressively and employs a dynamic, editable tree structure to help users organize and navigate their design space. Based on this paradigm, we developed a prototyping tool named InspirationGraph, focusing on the product design field. The results from a user study involving 24 participants highlight how this structured exploration approach supports divergent thinking and reduces cognitive load. We also uncover varying ideation patterns among designers and offer actionable insights into how T2I systems can be reimagined to better support the early-stage design.

In early-stage industrial design, teams generate essential but fragile process knowledge—semantic tags, sketches, exploration paths—that is rarely captured or reused but which may be useful at latter design stages, and AI could be used for this purpose. Yet existing AI creativity tools remain outcome-oriented, offering limited support for preserving, tracing, or recombining underlying reasoning. Our formative study (N=6) revealed persistent challenges in team–AI ideation across sessions and collaborators, including semantic–visual fragmentation, context loss, and cross-tool disruption. These insights inspired CoNode, a two-layer system that embeds AI nodes within a shared whiteboard through triplet workflows and augments them with workflow-level consolidation, reuse, and recombination via the CoSense module. We conducted a two-stage evaluation: User Study I (N=12) validates CoNode's foundational interaction paradigm layer, and User Study II (N=30) evaluates its process-oriented knowledge layer. Results show that CoNode significantly improves knowledge consolidation, reuse, and recombination, effectively facilitating the collaborative processes and demonstrating how generative AI can evolve process knowledge across collaborative rounds.

Video distortion seriously affects user experience and downstream tasks. Existing video restoration methods still suffer from high-frequency detail loss, limited spatio-temporal dependency modeling, and high computational complexity. In this letter, we propose a novel video restoration method based on full-frequency spatio-temporal information enhancement (FFSTIE). The proposed FFSTIE includes an implicit alignment module for accurate recovery of high-frequency details and a full-frequency feature reconstruction module for adaptive enhancement of frequency components. Comprehensive experiments with quantitative and qualitative comparisons demonstrate the effectiveness of our FFSTIE method. On the video deblurring dataset DVD, FFSTIE achieves 0.75% improvement in PSNR and 1.08% improvement in SSIM with 35% fewer parameters and 59% lower GMAC compared to VDTR (TCSVT'2023), achieving a balance between performance and efficiency. On the video denoising dataset DAVIS, FFSTIE achieves the best performance with an average of 35.36 PSNR and 0.9347 SSIM, surpassing existing unsupervised methods.

Natural video capturing suffers from visual blurriness due to high-motion of cameras or objects. Until now, the video blurriness removal task has been extensively explored for both human vision and machine processing. However, its computational cost is still a critical issue and has not yet been fully addressed. In this paper, we propose a novel Lightweight Video Deblurring (LightViD) method that achieves the top-tier performance with an extremely low parameter size. The proposed LightViD consists of a blur detector and a deblurring network. In particular, the blur detector effectively separate blurriness regions, thus avoid both unnecessary computation and over-enhancement on non-blurriness regions. The deblurring network is designed as a lightweight model. It employs a Spatial Feature Fusion Block (SFFB) to extract hierarchical spatial features, which are further fused by ConvLSTM for effective spatial-temporal feature representation. Comprehensive experiments with quantitative and qualitative comparisons demonstrate the effectiveness of our LightViD method, which achieves competitive performances on GoPro and DVD datasets, with reduced computational costs of 1.63M parameters and 96.8 GMACs. Trained model available: https://github.com/wgp/LightVid.