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RelightVid: Temporal-Consistent Diffusion Model for Video Relighting
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RelightVid: Temporal-Consistent Diffusion Model for Video Relighting
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Diffusion models have demonstrated remarkable success in image generation and editing, with recent advancements enabling albedo-preserving image relighting. However, applying these models to video relighting remains challenging due to the lack of paired video relighting datasets and the high demands for output fidelity and temporal consistency, further complicated by the inherent randomness of diffusion models. To address these challenges, we introduce RelightVid, a flexible framework for video relighting that can accept background video, text prompts, or environment maps as relighting conditions. Trained on in-the-wild videos with carefully designed illumination augmentations and rendered videos under extreme dynamic lighting, RelightVid achieves arbitrary video relighting with high temporal consistency without intrinsic decomposition while preserving the illumination priors of its image backbone.
Forward citations
Cited by 9 Pith papers
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TokenLight: Precise Lighting Control in Images using Attribute Tokens
TokenLight encodes lighting attributes as tokens in a conditional image generation model trained mostly on synthetic data, enabling precise relighting control and implicit learning of light-scene interactions.
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LiveLight: Real-time Streaming Video Relighting with Interactive Control
A diffusion-based system performs real-time, interactive video relighting by injecting multi-plane light irradiance conditions and streaming latent chunks.
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ID-V2V: Identity-Preserving Video Restylization
ID-V2V restyles video by conditioning a diffusion model on edited keyframes, depth, relit faces, and face normals, so scene edits propagate while facial identity and performance are preserved.
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Cinematic Compositing Using Character-Environment-Harmonized Video Generation Models
An end-to-end diffusion model generates backgrounds, relights green-screen actors, and replaces or creates props in one pass, improving over cascaded inpainting plus relighting baselines in user preference and auto metrics.
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Relit-LiVE: Relight Video by Jointly Learning Environment Video
Relit-LiVE jointly predicts relit videos and viewpoint-aligned environment maps inside a single diffusion process to achieve physically consistent video relighting without camera pose input.
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Toward Real-World Adoption of Portrait Relighting via Hybrid Domain Knowledge Fusion
Hybrid Domain Knowledge Fusion distills expertise from specialized models across synthetic, OLAT, and real datasets into a lightweight student model for state-of-the-art portrait relighting with 6x-240x faster inference.
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VibeFlow: Versatile Video Chroma-Lux Editing through Self-Supervised Learning
VibeFlow performs versatile video chroma-lux editing in zero-shot fashion by self-supervised disentanglement of structure and color-illumination cues inside pre-trained video models, plus residual velocity fields and ...
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Cinematic Compositing Using Character-Environment-Harmonized Video Generation Models
End-to-end video diffusion framework with tri-mask guidance and RGB-D denoising for joint modeling of character-to-environment physical interactions and environment-to-character lighting harmonization in cinematic com...
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HarmoVid: Relightful Video Portrait Harmonization
HarmoVid trains a video diffusion model on deflickered paired data from real and synthetic videos using asymmetric alpha mask conditioning to produce temporally coherent relightful portrait harmonization.
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