Zen Video: Generative Video at Scale

Text-to-image was just the beginning. Zen Video generates high-quality video from text prompts, images, and existing footage.

The Video Generation Challenge

Video is exponentially harder than images:

Temporal consistency: Objects must maintain identity across frames
Motion coherence: Movement must be physically plausible
Compute scale: 30 fps × resolution × duration = massive compute
Quality bar: Users notice small artifacts in motion

We've spent two years solving these problems.

Zen Video Capabilities

Text-to-Video

Prompt: "A golden retriever running through a meadow at sunset,
        slow motion, cinematic lighting"

Output: 10-second 1080p video, 24fps

Image-to-Video

Input: Single image of a person
Prompt: "The person turns their head and smiles"

Output: Animated version of the input image

Video-to-Video

Input: Existing footage
Prompt: "Transform to anime style, maintain motion"

Output: Style-transferred video with original motion

Video Extension

Input: 5-second clip
Prompt: "Continue the scene naturally"

Output: Extended video maintaining consistency

Technical Architecture

3D VAE: Compress video to latent space while preserving temporal structure.

Spatial-Temporal Transformer: Attention across both space and time dimensions.

Motion Encoding: Explicit motion representation for coherent movement.

Multi-Resolution: Progressive generation from coarse to fine.

Video → VAE Encoder → Latent (B×C×T×H×W) → Transformer → Latent → VAE Decoder → Video
                           ↑
                    Text Embeddings

Temporal Consistency

The hardest problem in video generation is maintaining consistency:

Cross-Frame Attention: Each frame attends to previous frames.

Motion Prediction: Explicit prediction of object trajectories.

Identity Preservation: Feature tracking across frames.

Physics Priors: Training on real-world physics for plausible motion.

Generation Modes

Mode	Duration	Resolution	Generation Time
Fast	4 sec	720p	30 sec
Standard	10 sec	1080p	2 min
Quality	30 sec	4K	15 min
Extended	60+ sec	1080p	Variable

Use Cases

Marketing: Generate product videos without expensive shoots.

Prototyping: Visualize concepts before production.

Entertainment: Concept art to animated sequences.

Education: Generate explanatory animations.

Social Media: Infinite content creation at scale.

Example Generations

Nature Documentary Style:

"Time-lapse of a flower blooming, macro photography,
 morning dew droplets, natural lighting"

Product Showcase:

"Sleek smartphone rotating on white background,
 studio lighting, reflective surface, 4K"

Character Animation:

"Cartoon character walking cycle, side view,
 smooth animation, colorful background"

Control and Editing

Zen Video offers fine-grained control:

Camera Motion: Pan, zoom, orbit, tracking shots.

Object Motion: Define trajectories for specific elements.

Style Transfer: Apply consistent style across frames.

Region Editing: Modify specific areas while preserving rest.

video = zen_video.generate(
    prompt="A city street at night",
    camera_motion="slow_pan_right",
    duration=10,
    style="cyberpunk",
    seed=42
)

Quality vs. Speed Trade-offs

More diffusion steps = better quality, slower generation:

Steps	Quality (FVD↓)	Time (10s clip)
20	145	30s
50	98	75s
100	76	150s
250	72	6min

We default to 50 steps for the best quality/speed balance.

What's Next

Image-to-Video-to-3D: Generate 3D scenes from single images via video.

Interactive Generation: Real-time video generation responding to input.

Longer Form: Feature-length consistent generation.

Audio Sync: Generate video that matches audio/music.

The future of video is generated. Zen Video is how we get there.

This post is part of our retrospective series exploring the technical foundations of Hanzo and Zen.