# Video Synthesis

AIGCPanel is a user-friendly one-stop AI digital human system accessible even for beginners. It supports video synthesis, audio synthesis, and voice cloning, simplifying local model management with one-click import and use of AI models. Product background indicates that AIGCPanel aims to enhance the efficiency of digital human material management by integrating various AI functionalities while lowering technical barriers, making it easy for non-professionals to manage and use AI digital humans. The product is based on AGPL-3.0 open-source license and is completely free to use.

AigcPanel is a straightforward and user-friendly one-stop AI digital persona system, supporting video synthesis, sound synthesis, and voice cloning, while simplifying local model management and one-click model integration. Leveraging the latest in artificial intelligence technology, it provides users with efficient and convenient solutions for creating digital personas, making it particularly suitable for professionals and enterprises needing to produce video and audio content. AigcPanel holds a firm position in the digital persona creation field thanks to its ease of use, efficiency, and powerful capabilities.

StableAnimator is the first end-to-end identity-preserving video diffusion framework that synthesizes high-quality videos without the need for post-processing. This technology ensures identity consistency through conditional synthesis based on reference images and a series of poses. Its main advantage is that it does not rely on third-party tools, making it suitable for users who need high-quality portrait animations.

MIMO is a versatile video synthesis model that can mimic any individual interacting with objects during complex motions. It synthesizes character videos with controllable attributes such as characters, actions, and scenes based on simple inputs provided by the user (e.g., reference images, pose sequences, scene videos, or images). MIMO achieves this by encoding 2D video into compact spatial codes and decomposing them into three spatial components (main subject, underlying scene, and floating occlusions). This method allows users to flexibly control spatial motion representation and create 3D perceptive synthesis, suitable for interactive real-world scenarios.

ViewCrafter is an innovative approach that leverages the generative capabilities of video diffusion models and the coarse 3D cues provided by point-based representations to synthesize high-fidelity new viewpoints of general scenes from single or sparse images. The method progressively expands the area covered by 3D cues and new viewpoints through iterative view synthesis strategies and camera trajectory planning algorithms, thereby increasing the generation range of new viewpoints. ViewCrafter can facilitate various applications, such as creating immersive experiences and real-time rendering by optimizing 3D-GS representations, as well as promoting imaginative content creation through scene-level text-to-3D generation.

SF-V is a diffusion-based video generation model that optimizes a pre-trained model through adversarial training, achieving the capability of generating high-quality videos in a single step. This model significantly reduces the computational cost of the denoising process while maintaining the temporal and spatial dependencies of video data, paving the way for real-time video synthesis and editing.

Align Your Steps is a method for optimizing the sampling time schedule of diffusion models (DMs). This approach utilizes stochastic calculus to find specific optimal sampling time schedules for different solvers, well-trained DMs, and datasets. It optimizes time discretization, i.e., sampling scheduling, by minimizing the KLUB term, thus improving the output quality within the same computation budget. This method performs exceptionally well in image, video, and 2D toy data synthesis benchmark tests, with optimized sampling schedules outperforming previously manually crafted schedules in almost all experiments.

Snap Video is a video-centric model that systematically addresses the challenges of motion fidelity, visual quality, and scalability in video generation by extending the EDM framework. Utilizing frame-level redundancy, the model proposes a scalable transformer architecture that represents the spatial and temporal dimensions as a highly compressed 1D latent vector. This allows for effective joint modeling of space and time, resulting in the synthesis of videos with strong temporal coherence and complex motion. This architecture enables the model to be efficiently trained to billions of parameters, achieving state-of-the-art results on multiple benchmarks.

Boximator is an intelligent video synthesis tool developed by Jiawei Wang, Yuchen Zhang, and others. It utilizes advanced deep learning techniques to generate rich and controllable video motion by adding text prompts and additional box constraints. Users can create unique video scenes through examples or custom text. Compared to other methods, Boximator utilizes supplementary box constraints from text prompts, providing more flexible motion control.

Lumiere is a text-to-video diffusion model designed to synthesize videos that exhibit realistic, diverse, and coherent motion, addressing key challenges in video synthesis. We introduce a spatio-temporal U-Net architecture that enables the generation of an entire video's temporal duration in a single model pass. This contrasts with existing video models, which synthesize distant keyframes and then perform temporal super-resolution, a method that intrinsically makes global temporal consistency difficult to achieve. By deploying spatial and, importantly, temporal downsampling and upsampling, and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate full-frame rate, low-resolution videos at multiple spatio-temporal scales. We demonstrate state-of-the-art results in text-to-video generation and showcase that our design readily facilitates a variety of content creation tasks and video editing applications, including image-to-video, video repair, and style generation.

FlowVid is an optical flow guided video synthesis model. By utilizing the spatial and temporal information of optical flow, it achieves temporal consistency between video frames. It seamlessly integrates with existing image synthesis models to enable various modification operations, including stylization, object swapping, and local editing. FlowVid boasts fast generation speed; a 4-second, 30FPS, 512×512 resolution video can be generated in just 1.5 minutes, outperforming CoDeF (3.1x), Rerender (7.2x), and TokenFlow (10.5x) respectively. In user evaluations, FlowVid achieved a quality score of 45.7%, significantly surpassing CoDeF (3.5%), Rerender (10.2%), and TokenFlow (40.4%).

gemelo is a generative AI platform that provides APIs for synthesizing voices, videos, and interactive virtual characters. With gemelo, you can generate realistic human voices, customize AI virtual characters, and create outstanding audio and video content for your business. gemelo's strength lies in its high-quality synthetic voices and videos, and you can achieve personalized customer interactions by training custom AI characters. Pricing is based on usage, see the official website for details.