FlowPortal: Residual-Corrected Flow for Training-Free Video Relighting and Background Replacement
- URL: http://arxiv.org/abs/2511.18346v1
- Date: Sun, 23 Nov 2025 08:45:17 GMT
- Title: FlowPortal: Residual-Corrected Flow for Training-Free Video Relighting and Background Replacement
- Abstract summary: FlowPortal is a training-free flow-based video relighting framework.<n>Our core innovation is a Residual-Corrected Flow mechanism that transforms a standard flow-based model into an editing model.<n> Experiments demonstrate that FlowPortal achieves superior performance in temporal coherence, structural preservation, and lighting realism.
- Score: 9.038689270820258
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Video relighting with background replacement is a challenging task critical for applications in film production and creative media. Existing methods struggle to balance temporal consistency, spatial fidelity, and illumination naturalness. To address these issues, we introduce FlowPortal, a novel training-free flow-based video relighting framework. Our core innovation is a Residual-Corrected Flow mechanism that transforms a standard flow-based model into an editing model, guaranteeing perfect reconstruction when input conditions are identical and enabling faithful relighting when they differ, resulting in high structural consistency. This is further enhanced by a Decoupled Condition Design for precise lighting control and a High-Frequency Transfer mechanism for detail preservation. Additionally, a masking strategy isolates foreground relighting from background pure generation process. Experiments demonstrate that FlowPortal achieves superior performance in temporal coherence, structural preservation, and lighting realism, while maintaining high efficiency. Project Page: https://gaowenshuo.github.io/FlowPortalProject/.
Related papers
- Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation [75.95737622994548]
Event cameras provide high-resolution motion cues that are well suited for bridging temporal gaps.<n>We propose an adapter-based framework that incorporates event-derived cues into a pre-trained image-to-video diffusion model.
arXiv Detail & Related papers (2026-08-11T04:47:44Z) - LiveLight: Real-time Streaming Video Relighting with Interactive Control [50.08584152649119]
We present LiveLight, the first diffusion-based framework for real-time streaming video relighting with interactive 3D lighting control.<n>We overcome three critical challenges: effectively injecting dynamic 3D lighting into a diffusion model, maintaining high-fidelity generation under a low NFE budget, and facilitating continuous streaming for interactive control.<n>Experiments on real-world and synthetic benchmarks demonstrate that LiveLight achieves state-of-the-art relighting quality while running at real-time speed.
arXiv Detail & Related papers (2026-08-03T06:45:00Z) - PAI-Studio: Cinematic Video Background Replacement with Camera-Aware Motion [51.8637101384136]
We present PAI-Studio, a new reference-conditioned video synthesis task.<n> generating dynamic backgrounds aligned with foreground motion while preserving foreground identity.<n>We further construct a 30K-scale dataset sourced from high-quality films and online videos to support this task.
arXiv Detail & Related papers (2026-05-31T18:45:11Z) - VibeFlow: Versatile Video Chroma-Lux Editing through Self-Supervised Learning [31.16852887085847]
This paper proposes VibeFlow, a novel self-supervised framework for chroma-lux editing.<n>We introduce a disentangled data pipeline that enforces the model to adaptively recombine structure from source videos and color-illumination cues from reference images.<n>Our framework generalizes in a zero-shot manner to diverse applications, including video relighting, recoloring, low-light enhancement, day-night translation, and object-specific color editing.
arXiv Detail & Related papers (2026-04-15T02:51:26Z) - SteerFlow: Steering Rectified Flows for Faithful Inversion-Based Image Editing [9.343095346668665]
SteerFlow is a model-agnostic editing framework with strong theoretical guarantees on source fidelity.<n>We introduce Trajectory Interpolation, which adaptively blends target-editing and source-reconstruction velocities to keep the editing trajectory anchored to the source.<n>Experiments on FLUX.1-dev and Stable Diffusion 3.5 Medium demonstrate that SteerFlow consistently achieves better editing quality than existing methods.
arXiv Detail & Related papers (2026-04-02T07:26:13Z) - RenderFlow: Single-Step Neural Rendering via Flow Matching [17.56739408578129]
We present a novel end-to-end, deterministic, single-step neural rendering framework, RenderFlow, built upon a flow matching paradigm.<n>Our method significantly accelerates rendering process and enhances both the physical plausibility and overall visual quality of the output.<n>The resulting pipeline achieves near real-time performance with photorealistic rendering quality, effectively bridging the gap between the efficiency of modern generative models and the precision of traditional physically based rendering.
arXiv Detail & Related papers (2026-01-11T14:28:46Z) - Light-X: Generative 4D Video Rendering with Camera and Illumination Control [52.87059646145144]
Light-X is a video generation framework that enables controllable rendering from monocular videos with both viewpoint and illumination control.<n>To address the lack of paired multi-view and multi-illumination videos, we introduce Light-Syn, a degradation-based pipeline with inverse-mapping.
arXiv Detail & Related papers (2025-12-04T18:59:57Z) - UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback [31.03901228901908]
We present UniLumos, a unified relighting framework for both images and videos.<n>We explicitly align lighting effects with the scene structure, enhancing physical plausibility.<n>Experiments demonstrate that UniLumos achieves state-of-the-art relighting with significantly improved physical consistency.
arXiv Detail & Related papers (2025-11-03T15:41:41Z) - DiTVR: Zero-Shot Diffusion Transformer for Video Restoration [48.97196894658511]
DiTVR is a zero shot video restoration framework that couples a diffusion transformer with trajectory aware attention and a flow consistent sampler.<n>Our attention mechanism aligns tokens along optical flow trajectories, with particular emphasis on vital layers that exhibit the highest sensitivity to temporal dynamics.<n>The flow guided sampler injects data consistency only into low-frequency bands, preserving high frequency priors while accelerating cache.
arXiv Detail & Related papers (2025-08-11T09:54:45Z) - Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video Enhancement [2.9695823613761316]
Low-light and underwater videos suffer from poor visibility, low contrast, and high noise.<n>Existing approaches typically rely on paired ground truth, which limits their practicality and often fails to maintain temporal consistency.<n>This paper introduces a novel zero-shot learning approach named Zero-TIG, leveraging the Retinex theory and optical flow techniques.
arXiv Detail & Related papers (2025-03-14T08:22:26Z) - Light-A-Video: Training-free Video Relighting via Progressive Light Fusion [52.420894727186216]
Light-A-Video is a training-free approach to achieve temporally smooth video relighting.<n>Adapted from image relighting models, Light-A-Video introduces two key techniques to enhance lighting consistency.
arXiv Detail & Related papers (2025-02-12T17:24:19Z) - RelightVid: Temporal-Consistent Diffusion Model for Video Relighting [95.10341081549129]
RelightVid is a flexible framework for video relighting.<n>It can accept background video, text prompts, or environment maps as relighting conditions.<n>It achieves arbitrary video relighting with high temporal consistency without intrinsic decomposition.
arXiv Detail & Related papers (2025-01-27T18:59:57Z) - Coherent Video Inpainting Using Optical Flow-Guided Efficient Diffusion [15.188335671278024]
We propose a new video inpainting framework using optical Flow-guided Efficient Diffusion (FloED) for higher video coherence.<n>FloED employs a dual-branch architecture, where the time-agnostic flow branch restores corrupted flow first, and the multi-scale flow adapters provide motion guidance to the main inpainting branch.<n>Experiments on background restoration and object removal tasks show that FloED outperforms state-of-the-art diffusion-based methods in both quality and efficiency.
arXiv Detail & Related papers (2024-12-01T15:45:26Z) - Taming Rectified Flow for Inversion and Editing [57.3742655030493]
Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation.<n>Despite their robust generative capabilities, these models often struggle with inaccuracies.<n>We propose RF-r, a training-free sampler that effectively enhances inversion precision by mitigating the errors in the inversion process of rectified flow.
arXiv Detail & Related papers (2024-11-07T14:29:02Z) - UniVST: A Unified Framework for Training-free Localized Video Style Transfer [102.52552893495475]
This paper presents UniVST, a unified framework for localized video style transfer based on diffusion models.<n>It operates without the need for training, offering a distinct advantage over existing diffusion methods that transfer style across entire videos.
arXiv Detail & Related papers (2024-10-26T05:28:02Z) - Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement [48.76608212565327]
This paper makes endeavors in the direction of learning for low-light video enhancement without using paired ground truth.
Compared to low-light image enhancement, enhancing low-light videos is more difficult due to the intertwined effects of noise, exposure, and contrast in the spatial domain, jointly with the need for temporal coherence.
We propose the Unrolled Decomposed Unpaired Network (UDU-Net) for enhancing low-light videos by unrolling the optimization functions into a deep network to decompose the signal into spatial and temporal-related factors, which are updated iteratively.
arXiv Detail & Related papers (2024-08-22T11:45:11Z) - Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition [52.89441679581216]
Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise.<n>We present an innovative video decomposition strategy that incorporates view-independent and view-dependent components.<n>Our framework consistently outperforms existing methods, establishing a new SOTA performance.
arXiv Detail & Related papers (2024-05-24T15:56:40Z) - Neural Video Portrait Relighting in Real-time via Consistency Modeling [41.04622998356025]
We propose a neural approach for real-time, high-quality and coherent video portrait relighting.
We propose a hybrid structure and lighting disentanglement in an encoder-decoder architecture.
We also propose a lighting sampling strategy to model the illumination consistency and mutation for natural portrait light manipulation in real-world.
arXiv Detail & Related papers (2021-04-01T14:13:28Z)
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.