Kling AI Avatar v2 Standard is an audio-driven image-to-video model developed by Kuaishou Technology as part of the Kling AI suite. It is designed specifically for generating avatar-style videos where a static image is animated to match an input audio track, producing talking head or character videos with synchronized lip movements and facial expressions. The model is optimized for consistent character performance rather than open-ended scene generation, making it ideal for content creators who need reliable, high-quality avatar animations without manual rigging or animation work.

The model operates on a dual-input architecture: a reference image (portrait, cartoon, animal, or stylized character) and an audio file. It uses the audio waveform to drive facial animation timing, ensuring that lip movements and subtle head motions are tightly synchronized with speech. This audio-first approach constrains the generation to character animation rather than general video synthesis, trading broad scene control for strong lip-sync precision and character consistency. It supports a wide range of character types, including realistic humans, animals, cartoons, and stylized illustrations, and is positioned as a cost-effective, production-ready solution for scalable avatar video generation.

Architecture: Kling AI Avatar v2 Standard

Parameters: Not publicly disclosed

Resolution: Output resolution not explicitly specified, but designed for standard portrait-oriented video suitable for social media and web content

Input/Output formats:

- Input image formats: JPG, JPEG, PNG, WebP, GIF, AVIF

- Input audio formats: MP3, OGG, WAV, M4A, AAC

- Output format: MP4 video with synchronized audio

Performance metrics:

- Output video duration matches the length of the input audio

- Generation cost scales linearly with audio duration (approximately $0.0562 per second of output)

- Designed for fast turnaround suitable for batch processing and high-volume workflows

• The model requires both an image and an audio file as mandatory inputs; it cannot generate avatar video from text or image alone.
• For best results, use high-quality, well-lit portrait images with clear facial features and minimal occlusions (e.g., hands, hair, or objects covering the mouth).
• Audio quality directly impacts lip-sync quality; use clean, clear speech recordings with minimal background noise and consistent volume.
• The model preserves the visual style and appearance of the input image, so stylistic choices (realistic, cartoon, anime, etc.) should be made at the image level.
• Overly long or complex audio inputs may lead to subtle degradation in expression consistency over time; shorter clips (10–30 seconds) often yield the most reliable results.
• Text prompts are optional and are used only to refine subtle aspects of the animation (e.g., emotion, expression, or head movement), not to control overall scene composition.
• The model is optimized for front-facing or slightly angled portraits; extreme angles, profiles, or heavily stylized faces may reduce lip-sync accuracy.

• Use a neutral or slightly smiling expression in the input image to give the model more flexibility in animating a range of emotions during speech.
• For consistent character performance across multiple clips, use the exact same image as input and keep lighting and pose as similar as possible.
• If the avatar appears too stiff, try adding a short text prompt like “natural expression, slight head movements, relaxed speaking” to encourage more lifelike motion.
• For expressive characters (cartoons, animals), ensure the mouth area is clearly visible and well-defined in the image to improve lip-sync accuracy.
• To create looping or seamless transitions, use the same starting and ending frame concept by keeping the avatar in a neutral pose at the beginning and end of the audio.
• For educational or presentation-style content, use short, clear audio segments and match the visual style of the avatar to the tone of the content (e.g., friendly cartoon for kids, professional human for business).
• When generating multiple avatars for a dialogue, use distinct but consistent images and ensure audio tracks are properly segmented and timed to avoid overlap or confusion.

• Generates high-quality, audio-synchronized avatar videos from a single image and audio input.
• Supports a wide range of character types: realistic humans, animals, cartoons, anime, and stylized illustrations.
• Produces natural lip-sync and facial expressions that closely match the timing and rhythm of the input speech.
• Maintains strong character consistency, preserving the exact appearance, style, and visual details of the input image.
• Automatically matches video duration to audio length, eliminating the need for manual timing adjustments.
• Handles subtle head movements and facial dynamics (blinks, eyebrow raises, etc.) in a natural, non-mechanical way.
• Suitable for commercial use, with outputs that meet broadcast-quality standards for social media, marketing, and educational content.
• Works reliably across different languages and accents, as long as the audio is clear and well-recorded.

• Creating talking head videos for YouTube, TikTok, and other social media platforms using a consistent branded avatar.
• Generating animated educational content with a recurring character (e.g., a cartoon teacher or mascot) that speaks over voiceover.
• Building AI-powered presentations or explainer videos where a character narrates slides or scripts.
• Developing interactive learning apps or e-learning modules with animated instructors or guides.
• Producing podcast visualizers or audiogram-style content with a synchronized character instead of static images.
• Animating cartoon or animal characters for short-form content, children’s stories, or brand mascots.
• Creating multilingual content by reusing the same character image with different language audio tracks.
• Developing internal training materials with a consistent virtual trainer or spokesperson.
• Prototyping character-driven narratives or storyboards where lip-sync accuracy is more important than complex camera motion.

• The model is audio-first and image-constrained, so it cannot change the character’s appearance, clothing, or background during the video.
• Extreme facial expressions in the input image (wide open mouth, exaggerated grimace) can sometimes lead to unnatural or distorted animations.
• Very low-resolution or heavily compressed images may result in blurry or inconsistent facial details in the output.
• Backgrounds in the input image are static; the model animates only the character’s face and head, not the environment.
• Some users report that very fast or mumbled speech can reduce lip-sync precision, so clear, moderate-paced speech works best.
• For long videos (over 30–60 seconds), there may be slight drift in expression consistency or subtle artifacts in facial motion.
• The model performs best with front-facing or three-quarter views; side profiles or extreme angles often produce weaker results.
• Positive user feedback highlights the reliability of lip-sync, the ease of use, and the strong character consistency across clips.
• Common concerns include the cost for very long videos and the lack of control over camera movement or scene changes during generation.

• Cannot generate general video scenes or camera movements; it is strictly an audio-synchronized avatar animation model.
• Limited ability to change the character’s appearance, pose, or environment during the video; the output is constrained to the input image.