Human-defined creativity is highly abstract, posing a challenge for multimodal large language models (MLLMs) to comprehend and assess creativity that aligns with human judgments. The absence of an existing benchmark further exacerbates this dilemma. To this end, we propose CreBench, which consists of two key components: 1) an evaluation benchmark covering the multiple dimensions from creative idea to process to products; 2) CreMIT (Creativity Multimodal Instruction Tuning dataset), a multimodal creativity evaluation dataset, consisting of 2.2K diverse-sourced multimodal data, 79.2K human feedbacks and 4.7M multityped instructions. Specifically, to ensure MLLMs can handle diverse creativity-related queries, we prompt GPT to refine the human feedback to activate stronger creativity assessment capabilities. CreBench serves as a foundation for building MLLMs that understand human-aligned creativity. Based on the CreBench, we fine-tune open-source general MLLMs, resulting in CreExpert, a multimodal creativity evaluation expert model. Extensive experiments demonstrate that the proposed CreExpert models achieve significantly better alignment with human creativity evaluation compared to state-ofthe-art MLLMs, including the most advanced GPT-4V and Gemini-Pro-Vision.

Recently, LiDAR-based fully sparse 3D object detection has gained great attention, which utilizes point clouds to boost efficiency. Nevertheless, the relationship between well-studied dense representation and fully sparse representation is under-explored in existing studies, which focuses solely on building sparse representation by feature diffusion to solve the notorious center point missing problem. To this end, we propose a dense-guided fully sparse detection scheme, named DGFSD, to bridge the gap between dense and sparse features by dense-guided diffusion. Different from prior studies, we propose DgD (Dense-guided Diffusion) to overcome the center feature missing problem by dense knowledge transferring. Specifically, DgD transfers high-quality central point features from dense representations to endow sparse representations with dense knowledge. Moreover, we customize DFW (Dense Feature Weighting) to express uninformative representation and lift foreground representation. It makes high-quality dense feature contribute more to arcuate regression. To the best of our knowledge, we are the first to explore dense knowledge's impact on fully sparse framework. Extensive experiments conducted on nuScenes and Argoverse2 benchmark demonstrate the effectiveness of the proposed method. Specifically, DGFSD achieves 71.6% NDS and 67.3% mAP on the nuScenes test benchmark. On Argoverse2, DGFSD achieves 40.6% mAP, outperforming previous best hybrid and fully sparse methods. The code is available at https://github.com/Raiden-cn/DGFSD.