Cailin Zhuang

Cailin Zhuang, Ailin Huang, Wei Cheng et al.

Story visualization aims to generate coherent image sequences that faithfully depict a narrative and align with character references. Despite progress in generative models, existing benchmarks are narrow in scope, often limited to short prompts, no character reference, or single-image cases, and fall short of real-world storytelling complexity. This hinders a nuanced understanding of model capabilities and limitations. We present ViStoryBench, a comprehensive benchmark designed to evaluate story visualization models across diverse narrative structures, visual styles, and character settings. The benchmark features richly annotated multi-shot scripts derived from curated stories spanning literature, film, and folklore. Large language models assist in story summarization and script generation, with all outputs verified by humans to ensure coherence and fidelity. Character references are carefully curated to maintain intra-story consistency across varying artistic styles. To enable thorough evaluation, ViStoryBench introduces a set of automated metrics that assess character consistency, style similarity, prompt adherence, aesthetic quality, and generation artifacts such as copy-paste behavior. These metrics are validated through human studies, and used to benchmark a broad range of open-source and commercial models. ViStoryBench offers a high-fidelity, multi-dimensional evaluation suite that facilitates systematic analysis and fosters future progress in visual storytelling.

Cailin Zhuang, Yaoqi Hu, Xuanyang Zhang et al.

3D Gaussian Splatting (3DGS) excels in photorealistic scene reconstruction but struggles with stylized scenarios (e.g., cartoons, games) due to fragmented textures, semantic misalignment, and limited adaptability to abstract aesthetics. We propose StyleMe3D, a holistic framework for 3D GS style transfer that integrates multi-modal style conditioning, multi-level semantic alignment, and perceptual quality enhancement. Our key insights include: (1) optimizing only RGB attributes preserves geometric integrity during stylization; (2) disentangling low-, medium-, and high-level semantics is critical for coherent style transfer; (3) scalability across isolated objects and complex scenes is essential for practical deployment. StyleMe3D introduces four novel components: Dynamic Style Score Distillation (DSSD), leveraging Stable Diffusion's latent space for semantic alignment; Contrastive Style Descriptor (CSD) for localized, content-aware texture transfer; Simultaneously Optimized Scale (SOS) to decouple style details and structural coherence; and 3D Gaussian Quality Assessment (3DG-QA), a differentiable aesthetic prior trained on human-rated data to suppress artifacts and enhance visual harmony. Evaluated on NeRF synthetic dataset (objects) and tandt db (scenes) datasets, StyleMe3D outperforms state-of-the-art methods in preserving geometric details (e.g., carvings on sculptures) and ensuring stylistic consistency across scenes (e.g., coherent lighting in landscapes), while maintaining real-time rendering. This work bridges photorealistic 3D GS and artistic stylization, unlocking applications in gaming, virtual worlds, and digital art.