arXiv Computer Vision
OmniXRef: A Unified Framework for Multimodal Multi-Reference Video Editing
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 21
OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation
OmniVBench introduces a comprehensive benchmark and a large-scale dataset for omni reference-to-video (R2V) generation, addressing gaps in existing evaluations that focus only on limited reference types and holistic consistency. The benchmark expands evaluation across 7 task families and 18 fine-grained tasks, covering content, motion, style, structure, narrative, and multi-reference settings, and employs a factor‑grounded evaluation with 12,172 checklist items to assess preservation, disentanglement, and routing of reference factors. The accompanying Omni‑R2V Dataset provides 340K training samples derived from professional video footage, along with task‑specific pipelines for scalable data construction, enabling broader research and revealing performance gaps in current R2V models.
arXiv Computer Vision
Aug 27
FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry
arXiv:2607.18227v2 Announce Type: replace Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
arXiv Computer Vision
Aug 27
RefVideo-6M: A Reliable Reference-Based Dataset for Instructional Video Editing
RefVideo-6M is a new large-scale reference-guided editing dataset that includes 5 million video editing samples and 1 million image editing samples, each paired with about 6 million visual references. The dataset is constructed to avoid artifacts by using real, artifact‑free videos as targets and filtering input conditions with multiple editing experts, thereby providing reliable supervision. It enables models to learn fine‑grained visual correspondence beyond text‑only instructions and supports the training of a reference‑guided video editing model, Ref‑MoT, which shows improved visual quality, controllability, and reference consistency.
arXiv Computer Vision
Sep 15
LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows
arXiv:2609.15863v1 Announce Type: new Abstract: Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizo...
arXiv AI
Oct 9
VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling
VINCIE‑NExT is a unified framework that transfers image editing capabilities to video editing by using in‑context visual demonstrations. It decomposes video editing into a chain of sub‑tasks (Video → Image → Image → Video), routing editing intent through the image domain and enabling joint training from heterogeneous image and video data under a single diffusion objective. The method introduces a novel position encoding to link image demonstrations and video frames, allowing pixel‑faithful appearance propagation, and supports test‑time scaling through progressive diffusion stages.
arXiv AI
Oct 12
MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing
MotionCoach is a multimodal agent designed to generate precise guidance for professional motion editing in videos. It constructs grounded prompts and reference clips through multi‑turn tool use and self‑verification, leveraging a training pipeline that includes a 10k SFT dataset and a 2k RL dataset optimized with Group Relative Policy Optimization. Evaluation on the VPMEBench benchmark shows significant performance gains, raising overall scores from 55.1 to 65.6 on Seedance 2.0 and from 51.2 to 64.5 on MiniMax‑H3.