NOVA: Sparse Control, Dense Synthesis for Pair-Free Video Editing
Recent video editing models have achieved impressive results, but most still require large-scale paired datasets. Collecting such naturally aligned pairs at scale remains highly challenging and constitutes a critical bottleneck, especially for local video editing data. Existing workarounds transfer image editing to video through global motion control for pair-free video editing, but such designs struggle with background and temporal consistency. In this paper, we propose NOVA: Sparse Control \& Dense Synthesis, a new framework for unpaired video editing. Specifically, the sparse branch provides semantic guidance through user-edited keyframes distributed across the video, and the dense branch continuously incorporates motion and texture information from the original video to maintain high fidelity and coherence. Moreover, we introduce a degradation-simulation training strategy that enables the model to learn motion reconstruction and temporal consistency by training on artificially degraded videos, thus eliminating the need for paired data. Our extensive experiments demonstrate that NOVA outperforms existing approaches in edit fidelity, motion preservation, and temporal coherence.
69a7a5f42833ff1122ff6463
Tianlin Pan
claimed_verified
2026-03-04T09:22:41.449Z
66221f7a608fb4e7f46ecadb
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/yMCsWyTkL4Xdxe18uwMYR.jpeg
Tianlin Pan
ldiex
user
ldiex
69a7a5f42833ff1122ff6464
Jiayi Dai
69a7a5f42833ff1122ff6465
Chenpu Yuan
69a7a5f42833ff1122ff6466
Zhengyao Lv
69a7a5f42833ff1122ff6467
Binxin Yang
69a7a5f42833ff1122ff6468
Hubery Yin
69a7a5f42833ff1122ff6469
Chen Li
69a7a5f42833ff1122ff646a
Jing Lyu
69a7a5f42833ff1122ff646b
Caifeng Shan
69a7a5f42833ff1122ff646c
Chenyang Si
NOVA: Sparse Control, Dense Synthesis for Pair-Free Video Editing
Recent video editing models have achieved impressive results, but most still require large-scale paired datasets. Collecting such naturally aligned pairs at scale remains highly challenging and constitutes a critical bottleneck, especially for local video editing data. Existing workarounds transfer image editing to video through global motion control for pair-free video editing, but such designs struggle with background and temporal consistency. In this paper, we propose NOVA: Sparse Control \& Dense Synthesis, a new framework for unpaired video editing. Specifically, the sparse branch provides semantic guidance through user-edited keyframes distributed across the video, and the dense branch continuously incorporates motion and texture information from the original video to maintain high fidelity and coherence. Moreover, we introduce a degradation-simulation training strategy that enables the model to learn motion reconstruction and temporal consistency by training on artificially degraded videos, thus eliminating the need for paired data. Our extensive experiments demonstrate that NOVA outperforms existing approaches in edit fidelity, motion preservation, and temporal coherence.
NOVA is a novel unpaired video editing framework that uses sparse semantic guidance and dense synthesis to achieve high-fidelity editing with improved motion preservation and temporal coherence.
video editing unpaired learning sparse control dense synthesis motion reconstruction temporal consistency degradation-simulation training