Visual effects (VFX) are essential visual enhancements fundamental to modern cinematic production. Although video generation models offer cost-efficient solutions for VFX production, current methods are constrained by per-effect LoRA training, which limits generation to single effects. This fundamental limitation impedes applications that require spatially controllable composite effects, i.e., the concurrent generation of multiple effects at designated locations. However, integrating diverse effects into a unified framework faces major challenges: interference from effect variations and spatial uncontrollability during multi-VFX joint training. To tackle these challenges, we propose Omni-Effects, a first unified framework capable of generating prompt-guided effects and spatially controllable composite effects. The core of our framework comprises two key innovations: (1) LoRA-based Mixture of Experts (LoRA-MoE), which employs a group of expert LoRAs, integrating diverse effects within a unified model while effectively mitigating cross-task interference. (2) Spatial-Aware Prompt (SAP) incorporates spatial mask information into the text token, enabling precise spatial control. Furthermore, we introduce an Independent-Information Flow (IIF) module integrated within the SAP, isolating the control signals corresponding to individual effects to prevent any unwanted blending. To facilitate this research, we construct a comprehensive VFX dataset Omni-VFX via a novel data collection pipeline combining image editing and First-Last Frame-to-Video (FLF2V) synthesis, and introduce a dedicated VFX evaluation framework for validating model performance. Extensive experiments demonstrate that Omni-Effects achieves precise spatial control and diverse effect generation, enabling users to specify both the category and location of desired effects.

Video generation models have achieved remarkable progress, particularly excelling in realistic scenarios; however, their performance degrades notably in imaginative scenarios. These prompts often involve rarely co-occurring concepts with long-distance semantic relationships, falling outside training distributions. Existing methods typically apply test-time scaling for improving video quality, but their fixed search spaces and static reward designs limit adaptability to imaginative scenarios. To fill this gap, we propose ImagerySearch, a dynamic test-time scaling law strategy inspired by imagery that adaptively adjusts the inference search space and reward guided by prompts, effectively enhancing generation quality in imaginative scenarios. Furthermore, we introduce LDT-Bench, the first benchmark targeting long-distance semantic prompts, designed to evaluate the creativity of video generation models. It comprises 2,839 challenging concept pairs from diverse recognition datasets and incorporates an automatic evaluation protocol to assess creative capacity. Extensive experiments on LDT-Bench demonstrate that our approach consistently outperforms general generation models and test-time scaling approaches. Additionally, ImagerySearch achieves strong performance on VBench, confirming its effectiveness in improving video generation quality under diverse conditions.

Air-writing has emerged as a promising communication modality for AR/VR and metaverse environments, enabling quiet, non-contact text input by translating finger movements into natural language. However, existing approaches typically project in-air writing onto a virtual 2D plane and assume characters are formed with a single continuous stroke–an oversimplification that neglects the rich 3D structure inherent in natural handwriting. In this work, we challenge the “single-stroke 2D” paradigm and explore the role of depth cues in enhancing air-writing recognition. To this end, we present DAAWBench, the first large-scale Depth-Aware Air-Writing dataset, featuring 8.8 million RGB-D frames annotated with 3,755 Chinese characters from the GB2312-80 Level-1 set. Our analysis reveals consistent depth variations at stroke boundaries, indicating that stroke segmentation and character recognition can benefit from depth modeling. Based on these insights, we propose DARec, a novel 3D trajectory-based recognition model that effectively leverages depth-aware priors. Extensive experiments across in-domain and out-of-domain settings, including evaluations with vision-language models (e.g., GPT-4o, Qwen-VL) and human baselines, show that DARec significantly outperforms 2D-only counterparts, achieving 87.73% accuracy versus 9.05%. Our findings demonstrate the critical importance of depth modeling in human-computer co-creative interfaces, and we will publicly release our dataset and code at https://github.com/wmeiqi/DAAWBench.