mlsanityData to 7 Oct 2026

VFX Creator: Animated Visual Effect Generation with Controllable Diffusion Transformer

arXiv 2502.05979 ↗First posted 9 Feb 2025cs.CVcited by 15

On the map: Diffusion models → Video generation

Abstract (arXiv)

Crafting magic and illusions is one of the most thrilling aspects of filmmaking, with visual effects (VFX) serving as the powerhouse behind unforgettable cinematic experiences. While recent advances in generative artificial intelligence have driven progress in generic image and video synthesis, the domain of controllable VFX generation remains relatively underexplored. In this work, we propose a novel paradigm for animated VFX generation as image animation, where dynamic effects are generated from user-friendly textual descriptions and static reference images. Our work makes two primary contributions: (i) Open-VFX, the first high-quality VFX video dataset spanning 15 diverse effect categories, annotated with textual descriptions, instance segmentation masks for spatial conditioning, and start-end timestamps for temporal control. (ii) VFX Creator, a simple yet effective controllable VFX generation framework based on a Video Diffusion Transformer. The model incorporates a spatial and temporal controllable LoRA adapter, requiring minimal training videos. Specifically, a plug-and-play mask control module enables instance-level spatial manipulation, while tokenized start-end motion timestamps embedded in the diffusion process, alongside the text encoder, allow precise temporal control over effect timing and pace. Extensive experiments on the Open-VFX test set demonstrate the superiority of the proposed system in generating realistic and dynamic effects, achieving state-of-the-art performance and generalization ability in both spatial and temporal controllability. Furthermore, we introduce a specialized metric to evaluate the precision of temporal control. By bridging traditional VFX techniques with generative approaches, VFX Creator unlocks new possibilities for efficient and high-quality video effect generation, making advanced VFX accessible to a broader audience.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 22 of its 49 references that are in the map. First posted on arXiv 9 Feb 2025.

Builds on

22 of 49 references.

Closest ancestors · 11

Same direction, newest first

  1. 2024LTX-Video: Realtime Video Latent Diffusion30 Dec
  2. 2024I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models26 May
  3. 2024Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models22 Feb
  4. 2024Latte: Latent Diffusion Transformer for Video Generation5 Jan
  5. 2023MotionCrafter: One-Shot Motion Customization of Diffusion Models8 Dec
  6. 2023DreamVideo: Composing Your Dream Videos with Customized Subject and Motion7 Dec
  7. 2023VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models1 Dec
  8. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
  9. 2023VideoCrafter1: Open Diffusion Models for High-Quality Video Generation30 Oct
  10. 2023LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation16 Oct
  11. 2023MotionDirector: Motion Customization of Text-to-Video Diffusion Models12 Oct

Other references · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2022Scalable Diffusion Models with Transformerscited by 5,931
  5. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  6. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,755
  7. 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,204
  8. 2024Open-Sora Plan: Open-Source Large Video Generation Modelcited by 286
  9. 2023DisCo: Disentangled Control for Realistic Human Dance Generationcited by 141
  10. 2024MotionBooth: Motion-Aware Customized Text-to-Video Generationcited by 92
  11. 2022Towards Understanding Mixture of Experts in Deep Learningcited by 52

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 8.

15 papers.

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  1. 2025PromptVFX: Text-Driven Fields for Open-World 3D Gaussian Animation
  2. 2026LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows
  3. 2023Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models
  4. 2024MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model
  5. 2023VDT: General-purpose Video Diffusion Transformers via Mask Modeling
  6. 2025VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate
  7. 2025Time-to-Move: Training-Free Motion Controlled Video Generation via Dual-Clock Denoising
  8. 2025Frame In-N-Out: Unbounded Controllable Image-to-Video Generation