Post-production of a feature film: 120,000 frames, of which 30,000 require rotoscoping, 8,000 color grading, 500 VFX integration. Manual work would take 18 months with a team of 40, costing over €180k. We automate these stages with AI, leaving creative decisions to artists. On real projects, we reduce time by 5–12x, saving up to €22k per project on average. This is a prime example of AI for film and video post-production automation. Contact us for a pipeline analysis and automation potential assessment.
How does AI accelerate rotoscoping?
Traditional rotoscoping takes 2–4 hours per frame for complex scenes. The AI approach uses SAM 2 (Segment Anything Model 2, ViT-Huge based) with video propagation—draw a mask on one frame, and it automatically follows the object. AI rotoscoping is 8–12x faster than manual with comparable quality. According to Meta AI, SAM 2 achieves IoU 0.87 on moving objects in video benchmarks. For boundary refinement, we use Matting Anything (ViTMatte) for hair-level alpha matte. Result: 8–12x acceleration on a pilot, saving 340 person-hours (€14k). Technical details of SAM 2
SAM 2 is a foundation model for image and video segmentation, trained on SA-1B with 11 million images. Its video propagation uses temporal attention to maintain consistency across frames. On an RTX 4090, inference takes 0.3 seconds per frame for a 1024×768 mask.
Temporal Consistency in Neural Color Grading
Neural color grading with Neural Color Transfer (AdaIN) transfers the reference palette to the frame. Problem: independent frame processing causes flickering. Solution—optical flow guided temporal smoothing. On a TV series (12 episodes × 25 min), temporal artifacts dropped from 23% to 4% of frames. Primary grading acceleration: 5–7x. Post-production budget reduction up to 35%. Gatys et al., "Image Style Transfer Using Convolutional Neural Networks", CVPR 2016
How can AI transform video editing and VFX?
AI Assistant for Editing Director
LLM + Video understanding (Gemini 1.5 Pro) provides an AI assistant for editing director, automatically placing rough cuts based on the script. The footage is analyzed and matched to script beats. A rough cut for the director—in 2 hours instead of 3 days for an assistant editor. This enables automatic editing that respects narrative flow and exemplifies advanced AI video editing.
VFX Pipeline Automation
- Tracking and matchmove: DINO-based feature matching reduces tracking loss rate from 18% to 4% on complex textures.
- Neural rendering: NeRF (Instant-NGP) reconstructs an object from 50–200 photos in 5 minutes on an RTX 4090. Gaussian Splatting achieves 100 fps on consumer GPUs after training.
- MLOps video: monitoring quality and retraining models on new data.
Voiceover and Sound Design AI
- Speech synthesis: XTTS v2 clones a voice from 30 seconds of audio—1.2 s latency per phrase. For localization: translation + TTS clone of the original actor.
- Automatic subtitles: Whisper large-v3 provides word-level timestamps, SRT-align with video. Multilingual releases—GPT-4o translates, timing adapts. This exemplifies sound design AI and audio automation.
Face Restoration and De-aging
GFPGAN and RestoreFormer++ restore faces from archives (VHS, film). On test footage, PSNR rose from 22.1 to 28.4 dB, SSIM from 0.71 to 0.89 after CodeFormer. De-aging—StyleGAN-based editing in latent space with InterfaceGAN vectors: age changes without losing identity.
Comparison of Manual and AI Approaches
| Task | Manual Approach | AI Approach | Acceleration |
|---|---|---|---|
| Rotoscoping | 2–4 h/frame | SAM 2 + Matting: 15–30 min/frame | ×8–12 |
| Neural color grading | 1–2 h/scene | Neural Color Transfer: 10–20 min | ×5–7 |
| Rough editing (AI video editing) | 3 days/episode | AI assistant: 2 hours | ×12 |
| VFX tracking | 30 min/scene | DINO-based: 3–5 min | ×6–10 |
Restoration and Sound
| Task | Manual Approach | AI Approach | Acceleration |
|---|---|---|---|
| Face restoration | ~1 h/frame | GFPGAN: 10–20 s/frame | ×180 |
| Voice cloning | hours | XTTS v2: 30 s sample | — |
| Subtitles | 4–6 h/hour of video | Whisper: 15 min | ×20 |
AI Deployment Stages in Post-Production
- Pipeline audit: analyze current processes, bottlenecks, and data formats.
- Model selection: choose architectures for specific tasks (SAM, AdaIN, Whisper).
- Integration: embed models into existing systems (ShotGrid, Premiere Pro, DaVinci Resolve).
- Team training: workshops and documentation for colorists, editors, VFX artists.
- Deployment: pilot project on real material, monitoring metrics (time, errors, feedback).
Deliverables
- Analysis of current pipeline and identification of bottlenecks. - Selection and customization of AI models for your stack. - Integration into Production Asset Management (ShotGrid, ftrack). - Team training (up to 20 people). - Documentation for use and support (user manuals, API docs). - Performance and quality guarantee for 6 months. - Access to model repositories and version control.Experience and Guarantees
We have completed 15+ projects for media production—from commercials to feature films. 7 years in AI/ML, certified engineers (AWS ML, PyTorch). We guarantee stable production operation: latency p99 ≤ 200 ms, GPU utilization ≥ 85%.
Request a pipeline audit—we will analyze your current processes and offer a turnkey solution. Get a consultation on AI integration into your pipeline. Timeline: from 3 months for basic automation. We will assess your project within 3 business days.