Sumin Shim

Tae Eun Choi, Sumin Shim, Junhyeok Kim et al.

Generative inbetweening (GI) seeks to synthesize realistic intermediate frames between the first and last keyframes beyond mere interpolation. As sequences become sparser and motions larger, previous GI models struggle with inconsistent frames with unstable pacing and semantic misalignment. Since GI involves fixed endpoints and numerous plausible paths, this task requires additional guidance gained from the keyframes and text to specify the intended path. Thus, we give semantic and temporal guidance from the keyframes and text onto each intermediate frame through Keyframe-anchored Attention Bias. We also better enforce frame consistency with Rescaled Temporal RoPE, which allows self-attention to attend to keyframes more faithfully. TGI-Bench, the first benchmark specifically designed for text-conditioned GI evaluation, enables challenge-targeted evaluation to analyze GI models. Without additional training, our method achieves state-of-the-art frame consistency, semantic fidelity, and pace stability for both short and long sequences across diverse challenges.

Yejin Choi, Jiwan Chung, Sumin Shim et al.

Visual text design plays a critical role in conveying themes, emotions, and atmospheres in multimodal formats such as film posters and album covers. Translating these visual and textual elements across languages extends the concept of translation beyond mere text, requiring the adaptation of aesthetic and stylistic features. To address this, we introduce a novel task of Multimodal Style Translation (MuST-Bench), a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems while preserving design intent. Our initial experiments on MuST-Bench reveal that existing visual text generation models struggle with the proposed task due to the inadequacy of textual descriptions in conveying visual design. In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions. SIGIL enhances image generation models through three innovations: glyph latent for multilingual settings, pretrained VAEs for stable style guidance, and an OCR model with reinforcement learning feedback for optimizing readable character generation. SIGIL outperforms existing baselines by achieving superior style consistency and legibility while maintaining visual fidelity, setting itself apart from traditional description-based approaches. We release MuST-Bench publicly for broader use and exploration https://huggingface.co/datasets/yejinc/MuST-Bench.

Jaehyun Jeon, Min Soo Kim, Jang Han Yoon et al.

User interface (UI) design goes beyond visuals, guiding user behavior and overall user experience (UX). Strategically crafted interfaces, for example, can boost sign-ups and drive business sales, underscoring the shift toward UI/UX as a unified design concept. While recent studies have explored UI quality evaluation using Multimodal Large Language Models (MLLMs), they largely focus on surface-level features, overlooking behavior-oriented aspects. To fill this gap, we introduce WiserUI-Bench, a novel benchmark for assessing models' multimodal understanding of UI/UX design. It includes 300 diverse real-world UI image pairs, each consisting of two design variants A/B-tested at scale by actual companies, where one was empirically validated to steer more user actions than the other. Each pair is accompanied one or more of 684 expert-curated rationales that capture key factors behind each winning design's effectiveness, spanning diverse cognitive dimensions of UX. Our benchmark supports two core tasks: (1) selecting the more effective UI/UX design by predicting the A/B test verified winner and (2) assessing how well a model, given the winner, can explain its effectiveness in alignment with expert reasoning. Experiments across several MLLMs show that current models exhibit limited nuanced reasoning about UI/UX design and its behavioral impact. We believe our work will foster research in UI/UX understanding and enable broader applications such as behavior-aware interface optimization.