Self-Refining Video Sampling
Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additional training on augmented data, which is computationally expensive and still limited in capturing fine-grained motion. In this work, we present self-refining video sampling, a simple method that uses a pre-trained video generator trained on large-scale datasets as its own self-refiner. By interpreting the generator as a denoising autoencoder, we enable iterative inner-loop refinement at inference time without any external verifier or additional training. We further introduce an uncertainty-aware refinement strategy that selectively refines regions based on self-consistency, which prevents artifacts caused by over-refinement. Experiments on state-of-the-art video generators demonstrate significant improvements in motion coherence and physics alignment, achieving over 70\% human preference compared to the default sampler and guidance-based sampler.
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Sangwon Jang
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2026-01-29T09:17:43.672Z
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Sangwon
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Taekyung Ki
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2026-01-27T09:00:25.179Z
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Taekyung Ki
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Jaehyeong Jo
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2026-01-27T14:29:04.004Z
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Jaehyeong Jo
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Saining Xie
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2026-01-27T14:29:18.724Z
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Saining Xie
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Jaehong Yoon
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2026-01-27T09:00:27.411Z
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Jaehong Yoon
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Sung Ju Hwang
Self-Refining Video Sampling
Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additional training on augmented data, which is computationally expensive and still limited in capturing fine-grained motion. In this work, we present self-refining video sampling, a simple method that uses a pre-trained video generator trained on large-scale datasets as its own self-refiner. By interpreting the generator as a denoising autoencoder, we enable iterative inner-loop refinement at inference time without any external verifier or additional training. We further introduce an uncertainty-aware refinement strategy that selectively refines regions based on self-consistency, which prevents artifacts caused by over-refinement. Experiments on state-of-the-art video generators demonstrate significant improvements in motion coherence and physics alignment, achieving over 70\% human preference compared to the default sampler and guidance-based sampler.
Self-refining video sampling improves motion coherence and physics alignment by using a pre-trained video generator as its own denoising autoencoder for iterative refinement with uncertainty-aware region selection.
video generators denoising autoencoder iterative inner-loop refinement self-refining video sampling uncertainty-aware refinement motion coherence physics alignment