AI Video Generation: Current Tools and Techniques

AI video generation has made remarkable progress. Here’s a practical guide to current tools and techniques.

Available Tools

Runway Gen-2

# Runway API example (conceptual)
import runway

client = runway.Client(api_key="your-key")

def generate_video_runway(prompt: str, duration_seconds: int = 4) -> bytes:
    """Generate video using Runway Gen-2."""

    result = client.generate(
        prompt=prompt,
        model="gen-2",
        duration=duration_seconds,
        aspect_ratio="16:9"
    )

    return result.video_bytes

Stable Video Diffusion

from diffusers import StableVideoDiffusionPipeline
import torch

pipe = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt",
    torch_dtype=torch.float16
)
pipe = pipe.to("cuda")

def image_to_video(image_path: str, num_frames: int = 25) -> list:
    """Generate video from image using SVD."""

    from PIL import Image
    image = Image.open(image_path).resize((1024, 576))

    frames = pipe(
        image,
        num_frames=num_frames,
        decode_chunk_size=8
    ).frames[0]

    return frames

Prompt Engineering for Video

video_prompt_templates = {
    "cinematic": "{subject}, cinematic lighting, dolly shot, film grain, 24fps",
    "product": "{product} on white background, smooth rotation, studio lighting",
    "nature": "{scene}, golden hour, slow motion, national geographic style",
    "abstract": "{concept}, fluid motion, generative art, seamless loop"
}

def craft_video_prompt(subject: str, style: str, motion: str) -> str:
    """Craft effective video generation prompt."""

    prompt = f"{subject}, {style} style, {motion} motion"

    # Add quality modifiers
    prompt += ", high quality, detailed, smooth motion"

    return prompt

Post-Processing Pipeline

def video_post_process(frames: list, output_path: str):
    """Post-process generated video frames."""

    import cv2
    import numpy as np

    # Interpolate frames for smoother motion
    interpolated = interpolate_frames(frames, factor=2)

    # Apply color correction
    corrected = [color_correct(f) for f in interpolated]

    # Upscale if needed
    upscaled = [upscale_frame(f) for f in corrected]

    # Write video
    write_video(upscaled, output_path, fps=30)

    return output_path

Best Practices

1. Start with strong input - Better prompts/images = better videos
2. Keep it short - Quality drops with length
3. Post-process - Upscaling and interpolation help
4. Iterate quickly - Generate many, select best
5. Combine techniques - Image gen + video gen

Conclusion

AI video generation is rapidly improving. Start with current tools, develop prompt expertise, and build workflows that can scale as technology advances.