EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation

• URL: http://arxiv.org/abs/2605.22051v1
• Date: Thu, 21 May 2026 06:38:22 GMT
• Title: EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation
• Abstract summary: EasyVFX is a framework that achieves realistic visual effects synthesis under stringent constraints.<n>We show that EasyVFX produces structurally consistent and visually stunning effects, proving that frequency-aware learning is a key catalyst for democratizing professional-grade VFX.
• Score: 50.24005970263923
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generating high-fidelity visual effects (VFX) typically demands massive datasets and prohibitive computational power due to the intricate coupling of spatial textures and temporal dynamics. In this paper, we introduce EasyVFX, a resource-efficient framework that achieves realistic VFX synthesis under stringent constraints. Our core philosophy lies in frequency-domain decomposition: we observe that the complexity of VFX can be significantly mitigated by decoupling high-frequency components, which represent intricate spatial appearances, from low-frequency components that encapsulate global motion dynamics. This spectral disentanglement transforms a high-dimensional learning problem into manageable sub-tasks, thereby lowering the optimization barrier and reducing data dependency. Building upon this insight, we propose a two-stage training paradigm. First, we design a Frequency-aware Mixture-of-Experts (Freq-MoE) architecture. By utilizing a soft routing mechanism, our model assigns specialized experts to distinct spectral bands, enabling them to cultivate robust priors for appearance and motion dynamics. This specialization allows the model to acquire foundational VFX knowledge with fewer GPU resources. Second, we introduce a Test-Time Training strategy powered by a novel Frequency-constraint Loss. This allows the pre-trained model to swiftly adapt to specific, unseen effects through localized optimizations, requiring only about 100 steps on a single GPU. Experimental results demonstrate that EasyVFX produces structurally consistent and visually stunning effects, proving that frequency-aware learning is a key catalyst for democratizing professional-grade VFX.

Related papers

• EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect Creation [27.31323449481923]
  EffectMaker is a unified reasoning-generation framework that enables reference-based VFX customization.<n>We construct EffectData, the largest high-quality synthetic dataset containing 130k videos across 3k VFX categories.<n>Experiments show EffectMaker achieves superior visual quality and effect consistency over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-06T08:09:14Z)
• VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning [67.44716618860544]
  We introduce VFXMaster, the first unified, reference-based framework for VFX video generation.<n>It recasts effect generation as an in-context learning task, enabling it to reproduce diverse dynamic effects from a reference video onto target content.<n>In addition, we propose an efficient one-shot effect adaptation mechanism to boost generalization capability on tough unseen effects from a single user-provided video rapidly.
  arXiv  Detail & Related papers  (2025-10-29T17:59:53Z)
• Temporal In-Context Fine-Tuning for Versatile Control of Video Diffusion Models [34.131515004434846]
  We introduce Temporal In-Context Fine-Tuning (TIC-FT), an efficient approach for adapting pretrained video diffusion models to conditional generation tasks.<n>TIC-FT requires no architectural changes and achieves strong performance with as few as 10-30 training samples.<n>We validate our method across a range of tasks, including image-to-video and video-to-video generation, using large-scale base models such as CogVideoX-5B and Wan-14B.
  arXiv  Detail & Related papers  (2025-06-01T12:57:43Z)
• FourierFlow: Frequency-aware Flow Matching for Generative Turbulence Modeling [10.73187148812722]
  We propose a novel generative turbulence modeling framework that enhances the frequency-aware learning by implicitly and explicitly mitigating spectral bias and common-mode noise.<n>FourierFlow comprises three key innovations. Firstly, we adopt a dual-branch backbone architecture, consisting of a salient flow attention branch with local-global awareness to focus on sensitive turbulence areas.<n>Thirdly, we leverage the high-frequency modeling capabilities of the masked auto-encoder pre-training and implicitly align the features of the generative model toward high-frequency components.
  arXiv  Detail & Related papers  (2025-06-01T06:59:27Z)
• Freqformer: Image-Demoiréing Transformer via Efficient Frequency Decomposition [83.40450475728792]
  We present Freqformer, a Transformer-based framework specifically designed for image demoir'eing through targeted frequency separation.<n>Our method performs an effective frequency decomposition that explicitly splits moir'e patterns into high-frequency spatially-localized textures and low-frequency scale-robust color distortions.<n>Experiments on various demoir'eing benchmarks demonstrate that Freqformer achieves state-of-the-art performance with a compact model size.
  arXiv  Detail & Related papers  (2025-05-25T12:23:10Z)
• FreSca: Scaling in Frequency Space Enhances Diffusion Models [55.75504192166779]
  This paper explores frequency-based control within latent diffusion models.<n>We introduce FreSca, a novel framework that decomposes noise difference into low- and high-frequency components.<n>FreSca operates without any model retraining or architectural change, offering model- and task-agnostic control.
  arXiv  Detail & Related papers  (2025-04-02T22:03:11Z)
• VFX Creator: Animated Visual Effect Generation with Controllable Diffusion Transformer [56.81599836980222]
  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.<n>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, and start-end timestamps for temporal control, and (ii) VFX Creator, a controllable VFX generation framework based on a Video Diffusion Transformer.
  arXiv  Detail & Related papers  (2025-02-09T18:12:25Z)
• HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces [71.1071688018433]
  Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces. We improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2Kx2K)
  arXiv  Detail & Related papers  (2023-12-05T22:04:49Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [73.50359502037232]
  VoxNeRF is a novel approach to enhance the quality and efficiency of neural indoor reconstruction and novel view synthesis.<n>We propose an efficient voxel-guided sampling technique that allocates computational resources to selectively the most relevant segments of rays.<n>Our approach is validated with extensive experiments on ScanNet and ScanNet++.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time [43.0484840009621]
  Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly on modeling static objects captured under multi-view settings. We present a novel Fourier PlenOctree (FPO) technique to tackle efficient neural modeling and real-time rendering of dynamic scenes captured under the free-view video (FVV) setting. We show that the proposed method is 3000 times faster than the original NeRF and over an order of magnitude acceleration over SOTA.
  arXiv  Detail & Related papers  (2022-02-17T11:57:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.