PickStyle: Video-to-Video Style Transfer with Context-Style Adapters
We address the task of video style transfer with diffusion models, where the
goal is to preserve the context of an input video while rendering it in a
target style specified by a text prompt. A major challenge is the lack of
paired video data for supervision. We propose PickStyle, a video-to-video style
transfer framework that augments pretrained video diffusion backbones with
style adapters and benefits from paired still image data with source-style
correspondences for training. PickStyle inserts low-rank adapters into the
self-attention layers of conditioning modules, enabling efficient
specialization for motion-style transfer while maintaining strong alignment
between video content and style. To bridge the gap between static image
supervision and dynamic video, we construct synthetic training clips from
paired images by applying shared augmentations that simulate camera motion,
ensuring temporal priors are preserved. In addition, we introduce Context-Style
Classifier-Free Guidance (CS-CFG), a novel factorization of classifier-free
guidance into independent text (style) and video (context) directions. CS-CFG
ensures that context is preserved in generated video while the style is
effectively transferred. Experiments across benchmarks show that our approach
achieves temporally coherent, style-faithful, and content-preserving video
translations, outperforming existing baselines both qualitatively and
quantitatively.
68e91f0f95e8e6771df38cd4
Soroush Mehraban
claimed_verified
2025-10-11T13:45:27.415Z
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Soroush Mehraban
user
SoroushMehraban
68e91f0f95e8e6771df38cd5
Vida Adeli
claimed_verified
2025-10-11T13:45:32.759Z
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/avatars/1d6366ba0a7a829ed4d5cc483f4335ac.svg
Vida Adeli 
user
vida-adl
68e91f0f95e8e6771df38cd6
Jacob Rommann
68e91f0f95e8e6771df38cd7
Babak Taati
claimed_verified
2025-10-13T10:09:56.073Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/kVVoiT21S3CrvvbPq7uzF.jpeg
Babak Taati
user
BabakTaati
68e91f0f95e8e6771df38cd8
Kyryl Truskovskyi
PickStyle: Video-to-Video Style Transfer with Context-Style Adapters
We address the task of video style transfer with diffusion models, where the
goal is to preserve the context of an input video while rendering it in a
target style specified by a text prompt. A major challenge is the lack of
paired video data for supervision. We propose PickStyle, a video-to-video style
transfer framework that augments pretrained video diffusion backbones with
style adapters and benefits from paired still image data with source-style
correspondences for training. PickStyle inserts low-rank adapters into the
self-attention layers of conditioning modules, enabling efficient
specialization for motion-style transfer while maintaining strong alignment
between video content and style. To bridge the gap between static image
supervision and dynamic video, we construct synthetic training clips from
paired images by applying shared augmentations that simulate camera motion,
ensuring temporal priors are preserved. In addition, we introduce Context-Style
Classifier-Free Guidance (CS-CFG), a novel factorization of classifier-free
guidance into independent text (style) and video (context) directions. CS-CFG
ensures that context is preserved in generated video while the style is
effectively transferred. Experiments across benchmarks show that our approach
achieves temporally coherent, style-faithful, and content-preserving video
translations, outperforming existing baselines both qualitatively and
quantitatively.
PickStyle uses diffusion models with style adapters and synthetic video clips to perform video style transfer from text prompts, preserving context and style.
diffusion models video style transfer style adapters self-attention layers conditioning modules synthetic training clips camera motion Context-Style Classifier-Free Guidance CS-CFG temporally coherent style-faithful content-preserving