Best AI Video Generation Platform in 2024

AIAnimation
4 Jan 202426:34

TLDRIn this comprehensive review, John Draper tests and compares four leading AI video generation platforms: RunwayML, Pabs, Decoherence, and Leonard.ai. He evaluates their capabilities in creating landscapes, characters, and different art styles, highlighting the pros and cons of each. Draper also explores the ease of use, output quality, and unique features like Pabs' negative prompting and RunwayML's motion brush. The video provides insights into the current state of AI video generation and the potential for future improvements.

Takeaways

  • 😀 The video compares four AI video generation platforms: RunwayML, Pabs, Decoherence, and Leonard.ai.
  • 🔍 Decoherence, now known as Deoh, offers models like Flicker, Fluid, and Turbo, with the latter utilizing stable video for instant image to video conversion.
  • 🎨 Pabs (previously known as P.art) has a user-friendly UI, allowing for easy generation and manipulation of video content with features like negative prompts and expand canvas.
  • 🌟 RunwayML is recognized for its advanced tools like motion brush and camera controls, excelling in landscape animations and cinematic shots.
  • 🆕 Leonard.ai's motion feature, built on stable diffusion, shows promise for the future with its real-time canvas and image to motion capabilities.
  • 📸 The video creator tests various images and styles, noting the strengths and limitations of each platform in handling motion and character animations.
  • 📊 The script discusses the importance of multiple generations to achieve the best results due to the variable output of AI platforms.
  • 🛠️ Tools like Topaz Video AI are mentioned for upscaling AI-generated videos to higher resolutions, enhancing the quality of the final output.
  • 💬 The video invites viewers to share their thoughts on the platforms and suggests features that could improve the AI video generation process.
  • 🔄 The platforms are all in competition, with Pabs and RunwayML leading, but with newcomers like Leonard.ai and Decoherence showing potential.
  • 🔮 The future of AI video generation is anticipated to include more control, longer content capabilities, and possibly integration with other AI technologies.

Q & A

  • What are the four AI video generation platforms discussed in the video?

    -The four AI video generation platforms discussed are RunwayML, Pabs, Decoherence (now called Deoh), and Leonard.

  • What is the unique feature of Deoh's turbo model mentioned in the video?

    -The unique feature of Deoh's turbo model is its near-instant image generation, where you can type in a text prompt, generate images, and then press a button to convert that into a video file.

  • How does the video script describe the output resolution of the video files from Deoh?

    -The video script describes the output resolution of the video files from Deoh as quite low, at 1024x576, and notes that there doesn't appear to be a way to change that at the moment.

  • What is the main feature of Pabs that the script finds impressive?

    -The main feature of Pabs that the script finds impressive is the new UI, which allows for easy creation of multiple generations, the use of negative prompts for refining output, and the ability to edit elements in the image and expand the canvas.

  • What is the motion brush feature in Runway ML, and how does it work?

    -The motion brush feature in Runway ML allows users to paint into their scene, specifying areas that they want to be animated. It gives extra control over the AI model, helping to dictate what parts of the scene should move.

  • What is the aspect ratio used in the video script for the image and video generation?

    -The aspect ratio used in the video script for the image and video generation is 16:9.

  • What is the video generation process like in Leonard, and what is its potential?

    -In Leonard, the video generation process involves creating an image first and then using the new motion tool to apply motion strength to it. Its potential is significant as it's built on stable video, and with future updates, it could become a strong contender in the AI video generation space.

  • How does the video script compare the character animation capabilities of Runway ML and Pabs?

    -The video script suggests that Runway ML has a slight edge in character animation, particularly with its motion brush feature, but acknowledges that Pabs is close behind, especially with its negative prompting and canvas expansion features.

  • What is Topaz Video AI, and how can it be used with AI video generations?

    -Topaz Video AI is a paid tool that uses AI to upscale images from very low resolution up to 8K. It can be used with AI video generations from any platform to enhance the resolution and remove jittery motions, improving the overall quality of the video output.

  • What does the script suggest for the future of AI video generation platforms?

    -The script suggests that the future of AI video generation platforms will likely involve more advanced features, such as negative prompts in Runway ML and the ability to apply multiple motion brush passes in a clip. It also anticipates competition from other companies like Mid Journey, Open AI, and Google Meta.

Outlines

00:00

🎨 AI Video Generation Platforms Comparison

The script introduces a comparative exploration of four AI video generation platforms: RunwayML, Pabs, Decoherence, and Leonard. The author plans to test various images and art styles to evaluate the capabilities and limitations of each platform, focusing on landscapes, characters, and different art styles. The goal is to assess the ease of use, output quality, and suitability for specific scenes and characters.

05:01

📹 Testing Decoherence's AI Video Generation

The script details the testing process of Decoherence, now known as Deoh, which offers models like Flicker, Fluid, and Turbo. The author uses pre-prepared images, including an anime-style Ninja Warrior, to test the platform's motion settings and image quality. The results vary, with some showing impressive motion and landscape handling, while others exhibit issues like low resolution and character distortion.

10:01

🖼️ Exploring Pabs with Its New Features

The author discusses Pabs, now accessible via a new website, which has garnered attention for its capabilities showcased in late 2023. Despite initial concerns, the author finds the platform's UI intuitive, allowing for multiple generations and the use of negative prompts to refine outputs. Features like modify region and expand canvas are highlighted, demonstrating Pabs' potential for creative storytelling and animation.

15:02

🌄 Runway ML's Video Generation Capabilities

Runway ML is examined as a long-standing leader in AI video and animation generation. The platform offers various tools, including Gen 1 and Gen 2 for video generation, with the latter providing more advanced features. The author appreciates the motion brush and camera controls but notes the platform's slow-motion feel and challenges with character animation, while acknowledging its strengths in landscape generation.

20:03

🦕 Trying Out Leonardo's Stable Diffusion-Powered Motion

Leonardo's new motion tool, powered by stable diffusion, is explored. The author demonstrates the real-time canvas for image generation and then moves on to test the motion feature with various images. The results show promise, with smooth camera movements and some animated elements, although detail fidelity is sometimes compromised. The potential for future improvements with stable diffusion updates is highlighted.

25:04

🛠️ Upscaling AI Video Generations with Topaz Video AI

The script concludes with a demonstration of Topaz Video AI, a tool for upscaling AI-generated videos to higher resolutions. The author upscales a dinosaur clip from Leonardo, showcasing the software's ability to enhance image quality and add stabilization. The script wraps up by reiterating the competitive landscape of AI video generation platforms and encouraging viewers to share their thoughts on the platforms discussed.

Mindmap

Keywords

💡AI video generation platforms

AI video generation platforms refer to software or online services that utilize artificial intelligence to create videos based on user input. In the video, the host compares several of these platforms, highlighting their capabilities to generate content like landscapes, characters, and different art styles. The platforms mentioned include RunwayML, Pabs, Decoherence, and Leonard.ai, each with unique features and strengths in creating animated content.

💡Decoherence (decohere)

Decoherence, now rebranded as decohere, is an AI video generation platform that offers various models for video creation, such as flicker, fluid, and turbo models. The script discusses the platform's ability to generate videos from text prompts and images, with a focus on the turbo model's use of stable video technology. The platform's ease of use and the quality of its output are central to the video's evaluation of AI video generation capabilities.

💡Stable Video

Stable Video is an underlying technology utilized by some AI platforms to create fluid and stable video animations. It is highlighted in the context of decohere's turbo model and Leonard.ai's motion feature. The script mentions the potential of stable video to enhance the motion and fluidity of AI-generated videos, despite current limitations in output resolution and motion control.

💡RunwayML

RunwayML is an established AI video and animation generation platform that offers a suite of tools for creating images, removing backgrounds, and applying motion styles. The script praises its motion brush feature and camera controls, which allow for detailed direction over video generation. RunwayML's ability to produce cinematic and landscape animations is a key point in the video's comparison.

💡Pabs

Pabs, or P.art, is a newer AI video generation platform that gained attention for its capabilities showcased in promotional material. The script notes its user interface, which facilitates the organization of video generations and the application of positive and negative text prompts to guide the AI in creating desired outcomes. Pabs is positioned as a strong competitor to RunwayML.

💡Text prompts

Text prompts are user-provided descriptions that guide AI platforms in generating specific content. The script discusses how text prompts are used across the platforms to dictate the style, motion, and elements of the generated videos. They are essential for aligning the AI's output with the user's vision.

💡Leonard.ai

Leonard.ai is an AI platform that recently introduced a motion feature powered by stable diffusion technology. The script examines its capabilities, including real-time canvas and image-to-motion conversion. While it is still developing, the platform shows promise for future advancements in AI video generation.

💡Camera controls

Camera controls refer to the features within AI video generation platforms that allow users to dictate the movement and perspective of the virtual camera within the generated video. The script emphasizes the importance of these controls in creating dynamic and engaging video content, with platforms like RunwayML and Pabs offering sophisticated camera control options.

💡Upscaling

Upscaling is the process of increasing the resolution of a video or image. The script mentions Topaz Video AI as a tool for upscaling AI-generated videos to higher resolutions, such as 4K, to improve quality. This is particularly useful for enhancing the output from platforms that produce lower resolution videos initially.

💡Cinematic shots

Cinematic shots describe the visual composition and camera techniques used in film and video production to create a dramatic or aesthetically pleasing effect. The script praises RunwayML for its ability to produce high-quality cinematic shots in AI-generated videos, particularly in landscape animations.

💡Negative prompts

Negative prompts are text instructions that specify what should be avoided or not included in the AI-generated content. The script highlights Pabs' use of negative prompts as a way to refine video generations and prevent unwanted elements or styles from appearing in the final output.

Highlights

Comparison of four leading AI video generation platforms: RunwayML, Pabs, Decoherence, and Leonard.

Introduction of Decoherence's new motion ability and its three models: Flicker, Fluid, and Turbo.

Demonstration of Decoherence's instant image generation and conversion to video.

Testing of pre-prepared images for motion settings across different platforms.

Analysis of video resolution and upscaling capabilities of Decoherence's output.

Pabs' new P. Art website and its features for video generation.

Exploration of Pabs' UI for organization and refinement of video generations.

Showcasing of Pabs' modify region and expand canvas features for creative control.

Discussion on the potential of Pabs for 2D animation and storytelling.

Overview of Runway ML's history and its advancements in AI video generation.

Explanation of Runway ML's Gen 2 model and its motion control features.

Use of Runway ML's motion brush for directing animation in video generation.

Comparison of Runway ML's output quality for landscapes and character animations.

Introduction to Leonard's AI video generation tool and its capabilities.

Leonard's real-time canvas and image generation process explained.

Assessment of Leonard's motion feature and its potential for future updates.

Use of Topaz Video AI for upscaling AI-generated videos from various platforms.

Final thoughts on the current state of AI video generation platforms and future prospects.