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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2da8f89ab7f7cadd0d21177e299cf58bCVF · CVPR 2026

EasyV2V: A High-quality Instruction-based Video Editing Framework

Mai, Jinjie, Wang, Chaoyang, Qian, Gordon Guocheng, Menapace, Willi, Tulyakov, Sergey, Ghanem, Bernard, Wonka, Peter, Mirzaei, Ashkan

原文摘要

While image editing has advanced rapidly, video editing remains less explored, facing challenges in consistency, control, and generalization.We study the design space of data, architecture, and control, and introduce EasyV2V, a simple and effective framework for instruction-based video editing. On the data side, we compose existing experts with fast inverses to build diverse video pairs, lift image edit pairs into videos via single-frame supervision and pseudo pairs with shared affine motion, mine dense-captioned clips for video pairs, and add transition supervision to teach how edits unfold.On the model side, we observe that pretrained text-to-video models possess editing capability, motivating a simplified design. Simple sequence concatenation for conditioning with light LoRA fine-tuning suffices to train a strong model.For control, we unify spatiotemporal control via a single mask mechanism and support optional reference images.Overall, EasyV2V works with flexible inputs, e.g., video+text, video+mask+text, video+mask+reference+text, and achieves state-of-the-art video editing results, surpassing concurrent and commercial systems.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:4fa933ae2b08be477ff83ec26240be41CVF · CVPR 2026

EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing

Li, Runjia, Haji-Ali, Moayed, Mirzaei, Ashkan, Wang, Chaoyang, Sahni, Arpit, Skorokhodov, Ivan, Siarohin, Aliaksandr, Jakab, Tomas 等 12 位作者

原文摘要

We study instruction-guided editing of egocentric videos for interactive AR applications. While recent AI video editors perform well on third-person footage, egocentric views present unique challenges -- including rapid egomotion, and frequent hand-object interactions -- that create a significant domain gap. Moreover, existing offline editing pipelines suffer from high latency, limiting real-time interaction.To address these issues, we present a complete ecosystem for egocentric video editing. First, we construct EgoEditData, a carefully designed and manually curated dataset specifically designed for egocentric editing scenarios, featuring rich hand-object interactions, while explicitly preserving hands. Second, we develop EgoEdit, an instruction-following egocentric video editor that supports real-time streaming inference on a single GPU. Finally, we introduce EgoEditBench, an evaluation suite targeting instruction faithfulness, hand and interaction preservation, and temporal stability under egomotion.Across both egocentric and general editing tasks, EgoEdit produces temporally stable, instruction-faithful results with interactive latency. It achieves clear gains on egocentric editing benchmarks--where existing methods struggle--while maintaining performance comparable to the strongest baselines on general editing tasks. EgoEditData and EgoEditBench will be made public for the research community.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ea8fe2fc403dab52b3386ebd7c8fd52eCVF · CVPR 2026

RigMo: Unifying Rig and Motion Learning for Generative Animation

Zhang, Hao, Luo, Jiahao, Wan, Bohui, Zhao, Yizhou, Li, Zongrui, Vasilkovsky, Michael, Wang, Chaoyang, Wang, Jian 等 10 位作者

原文摘要

Despite significant progress in 4D generation, rig and motion--the core structural and dynamic components of animation--are typically modeled as separate problems. Existing pipelines rely on ground-truth skeletons and skinning weights for motion generation and treat auto-rigging as an independent process, undermining scalability and interpretability. We present RigMo, a unified generative framework that jointly learns rig and motion directly from raw mesh sequences, without any human-provided rig annotations. RigMo encodes per-vertex deformations into two compact latent spaces: a rig latent that decodes into explicit Gaussian bones and skinning weights, and a motion latent that produces time-varying SE(3) transformations. Together, these outputs define an animatable mesh with explicit structure and coherent motion, enabling feed-forward rig and motion inference for deformable objects. Beyond unified rig-motion discovery, we introduce a Motion-DiT model operating in RigMo's latent space and demonstrate that these structure-aware latents can naturally support downstream motion generation tasks. Experiments on DeformingThings4D, Objaverse-XL, and TrueBones demonstrate that RigMo learns smooth, interpretable, and physically plausible rigs, while achieving superior reconstruction and category-level generalization compared to existing auto-rigging and deformation baselines. RigMo establishes a new paradigm for unified, structure-aware, and scalable dynamic 3D modeling

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗