Ovi is an advanced, open-source AI model for unified audio-video generation, developed by Character.AI and introduced in 2025. It is designed to generate synchronized, cinematic video and audio content from text prompts or a combination of text and images. Unlike traditional pipelines that treat video and audio as separate outputs, Ovi models both modalities as a single generative process, ensuring natural synchronization and coherence between visuals and sound.

The core innovation of Ovi lies in its twin-backbone architecture, where identical DiT (Diffusion Transformer) modules are used for both audio and video branches. These branches are fused using blockwise cross-modal fusion, allowing for fine-grained exchange of timing and semantic information. This enables Ovi to produce high-quality, movie-grade video clips with realistic speech, emotional expression, and context-matched sound effects. Ovi is particularly notable for its ability to handle complex scenarios such as multi-person dialogues, precise lip-sync, and contextual sound generation, all within a unified framework.

• Architecture: Twin-DiT modules with blockwise cross-modal fusion
• Parameters: Approximately 11 billion (5B visual, 5B audio, 1B fusion)
• Resolution: Supports up to 960×960 (e.g., 720×1280, 704×1344, 720×720); default is 720×720 at 24 FPS
• Input/Output formats: Inputs can be text-only or text plus image; outputs are 5-second video clips with synchronized audio
• Performance metrics: Generates 5-second videos at 24 FPS; high-quality audio and video synchronization; optimized for short-form, cinematic content

• Ovi excels at human-centric scenarios due to its training data bias; best results are achieved with prompts involving people, dialogue, or emotional expression
• For optimal output, provide detailed prompts specifying scene, characters, camera movement, and mood
• Use embedded tags to control speech and background audio (e.g., for speech, for audio cues)
• Outputs may vary between runs due to stochastic generation; try multiple random seeds for best results
• High spatial compression and large model size balance speed and memory usage, but may limit extremely fine details or complex textures
• Best suited for short-form content (5 seconds); longer sequences may require stitching or post-processing

• Use explicit speech tags ( ... ) to generate dialogue with accurate lip-sync and speaker differentiation
• Describe background sounds or music using ... to guide contextual audio generation
• For multi-person scenes, specify each speaker and their lines separately to achieve realistic conversations
• Adjust aspect ratios (e.g., 9:16, 16:9, 1:1) to match the intended output format; higher resolutions (up to 960×960) can improve visual quality
• Iterate with different random seeds to select the most coherent or visually appealing result
• For best lip-sync and emotional expression, provide clear, concise speech content and specify emotions or tone in the prompt
• Avoid overly complex scenes with many tiny objects, as fine detail may be lost due to model compression

• Generates synchronized video and audio from text or text+image prompts in a single unified process
• Supports high-quality, context-matched speech, sound effects, and background music
• Achieves precise lip synchronization without explicit face bounding boxes
• Handles multi-person dialogue and multi-turn conversations naturally
• Produces cinematic, movie-grade short video clips with diverse emotions and large motion ranges
• Flexible aspect ratio and resolution support for various creative needs
• Open-source release with pretrained weights and inference code for research and development

• Creating talking avatars and digital humans for entertainment, marketing, or virtual assistants
• Generating short cinematic video clips with synchronized dialogue and sound for storytelling or advertising
• Producing educational content with animated speakers and contextual audio cues
• Rapid prototyping of video ideas for filmmakers, animators, and content creators
• Developing interactive media, such as conversational agents or immersive experiences
• Showcasing creative projects involving music videos, sound effects, or emotional performances
• Enabling research in multimodal AI, audio-visual synthesis, and human-computer interaction

• Some users report that Ovi's outputs are most consistent and high-quality in human-centric scenarios; non-human or abstract prompts may yield less coherent results
• The model's reliance on high spatial compression can limit the rendering of intricate textures or very small details
• Video length is currently limited to 5 seconds per generation; longer content requires manual assembly
• Resource requirements are significant due to the 11B parameter size; high-end GPUs are recommended for local inference
• Outputs can vary between runs; users often try multiple seeds to select the best result
• Community feedback highlights the model's impressive lip-sync and emotional expressiveness, especially for dialogue-driven content
• Some users note occasional artifacts or inconsistencies in complex scenes, particularly with rapid motion or crowded backgrounds
• Positive reviews emphasize the ease of generating synchronized audio and video, as well as the open-source availability for experimentation

• Limited to short-form (5-second) video generation; not optimal for long-form or continuous video production
• Visual detail and texture complexity are constrained by model compression and architecture, making it less suitable for scenes with many small or intricate elements
• Best performance is achieved with human-centric prompts; less effective for non-human, abstract, or highly technical scenarios

Output Format: MP4