Video Editing Models
Explore the best AI models for video editing
Gemini Omni Flash
NewGemini Omni Flash is Google DeepMind's groundbreaking multimodal AI model that generates physics-aware video with synchronized audio from any combination of text, images, video, and audio inputs. Announced at Google I/O 2026, it represents a paradigm shift from traditional text-to-video models by enabling conversational, iterative video editing — users can refine scenes through natural language without regenerating from scratch. The model maintains character consistency and scene memory across multiple editing rounds, preserves identity and voice throughout sequences, and understands real-world physics including gravity, collisions, and material properties. Omni Flash supports cinematic camera controls (dolly zoom, over-shoulder shots, tracking), accurate text rendering with word-by-word animation, multi-input synthesis (combining videos, images, audio, and storyboards), and style transfer across artistic mediums including anime, claymation, and watercolor. Built on Gemini's training data, it carries significantly more world knowledge than standalone video models like Veo, enabling it to visualize complex concepts from quantum computing to historical events without exhaustive prompting. Available through the Gemini app, Google Flow, and Google AI Studio, it produces clips up to 10 seconds with invisible SynthID watermarking for content authenticity.
ProPainter
ProPainter is an advanced deep learning model developed by S-Lab at Nanyang Technological University for video inpainting and object removal with exceptional temporal consistency. The model employs a dual-domain propagation architecture combined with Transformer-based attention to fill in masked or removed regions across video frames while maintaining seamless visual continuity. ProPainter takes a video and a binary mask indicating regions to be removed or filled, then generates the completed video with content that naturally blends with surrounding pixels and remains consistent across frames. The dual-domain approach propagates information in both spatial and temporal dimensions, using optical flow-guided warping to transfer texture details from neighboring frames and Transformer attention to synthesize content for regions with no visible reference. This combination allows ProPainter to handle challenging scenarios including large masked areas, fast camera motion, and complex scene dynamics that cause previous methods to produce flickering or ghosting artifacts. The model achieves state-of-the-art results on standard video inpainting benchmarks including DAVIS and YouTube-VOS, significantly outperforming previous approaches in both quantitative metrics and perceptual quality. Released under the S-Lab license, the model is open source for research purposes. Practical applications include removing unwanted objects or people from video footage, restoring damaged or corrupted video content, removing watermarks, creating clean background plates for visual effects compositing, and video-based content moderation. ProPainter integrates with standard video processing pipelines and can process videos at practical speeds on modern GPUs.