Every VFX director has faced a task where a 10-second shot takes 40 hours of rotoscoping. We automate that. We build automated pipelines for specific classes of VFX tasks. Neural network approaches do not replace VFX artists on large projects, but they radically change the economics of low-budget productions, commercials, and social media content. Our systems integrate directly into NLEs, cutting routine operation time by 60–90% and reducing production costs. Ready to evaluate your project? Get a consultation.
Which VFX Tasks Does AI Handle Better Than Humans?
Rotoscoping and masks: We use SAM 2 (Segment Anything Model 2) for automatic tracking segmentation of objects in video. Accuracy: IoU > 0.92 for static objects, > 0.85 for fast motion. Savings: a 10-minute video task with manual rotoscoping (40+ hours) is reduced to 2–4 hours with a semi-automatic system. The benefit is clear: instead of weeks of work, one day.
Background replacement / Environment generation: We apply Stable Video Diffusion + ControlNet to generate background environments. Inpainting ensures seamless background replacement with lighting consideration, and Neural HDR matching aligns object lighting with the new background.
Particle Effects & Simulation: StyleGAN-based generation of smoke, fire, and explosion textures. Neural simulation replaces Houdini simulations with inference—parametric control over intensity, color, and direction.
De-aging / Re-aging: StyleCLIP + GFPGAN for age correction of faces. Face Restoration (CodeFormer, GFPGAN v1.4) for upscaling and retouching. Natural results on 90% of frames without manual edits.
Wire Removal & Object Removal: LaMa (Large Mask inpainting) + Stable Diffusion inpainting. Automatic detection of wires/rigs via Grounding DINO. Processing speed: 10–30 frames/min on an RTX 4090.
Why Is Our Pipeline More Efficient Than Manual Work?
Performance comparison on RTX 4090:
| VFX Task | Speed (RTX 4090) | vs. Manual |
|---|---|---|
| Rotoscoping (SAM 2) | 15–25 frames/sec | -80% time |
| Inpainting (4K) | 3–8 sec/frame | -60% time |
| Face restoration | 25–30 frames/sec | -90% time |
| Background swap | 2–5 sec/frame | -70% time |
| Task | Traditional Method | AI Pipeline |
|---|---|---|
| Wire removal from 4K shot | 40 min manual rotoscoping + cloning | 3 sec inference + 2 min edits |
| Complex hair mask | 1 hour manual work | 10 sec SAM 2 + 5 min refinement |
| Background generation for green screen | 2 hours light setup and compositing | 10 sec generation + 1 min color correction |
Inference speed is several times faster than manual labor—especially with batch processing. Experience shows AI performs routine operations 5–10 times faster.
How We Build the Pipeline: Process
- Analysis and Audit (weeks 1–3): Audit client VFX tasks, test baseline models on sample footage. Identify priority effect classes.
- Custom Pipeline Development (weeks 4–8): Adapt to project specifics—genre, lighting, camera motion. Fine-tune if needed.
- NLE Integration (weeks 9–11): Connect Adobe Premiere Pro (CEP Extension), DaVinci Resolve (Fusion Script), After Effects (ExtendScript + Python bridge).
- Performance Optimization (weeks 12–14): Configure batch processing for production volumes. Guarantee stable performance under load.
What's Included in Deliverables
- A functional AI pipeline integrated into your NLE.
- Documentation for operation and configuration.
- Team training (2–3 sessions).
- Technical support for 1 month after launch.
- Pipeline source code (if required).
Timeline and Cost
Estimated timeline: 12 to 14 weeks turnkey. Cost is calculated individually—depends on the number of VFX classes, integration complexity, and footage volume. Get a consultation—we'll evaluate your project. Contact us for a demo.
What Remains for the Artist
Creative decisions: effect concept, art direction, handling non-standard situations. AI takes over the technical execution of template tasks. Final checking and correction remain mandatory—especially for hero shots. Over 5 years of experience in AI VFX, over 20 implementations—trust the routine to machines.
How Do We Ensure Stable Pipeline Operation?
We monitor quality metrics (IoU, PSNR) at each stage. When accuracy drops below a threshold, an automatic alert is sent to the operator. For production environments, we containerize modules (Docker + Kubernetes), simplifying scaling under peak loads. After deployment, we provide an SLA with incident response times.
Read more about the SAM 2 model in the official repository.