How AI Is Reshaping Visual Effects Budgets

Production studio giants, like Netflix, are embracing AI in production. In the Netflix Original Series, The Eternaut, AI was used to generate a building destruction scene ten times faster than traditional VFX and keep costs for this VFX within budget. Media houses on a tight schedule with a limited budget and hardware are forced to compromise on ambitious projects requiring high-quality visual effects. But with AI VFX tools, this no longer needs to be the case. This means more than saving time or money; it also means reclaiming creative control. Even with modest hardware, cinematic visual effects that once demanded enormous budgets and large teams can be generated. Video editors and content creators can adopt AI VFX to take their visual storytelling to the next level.

Dive into AI VFX

AI is reshaping the way that media houses approach visual effects. Here is how AI is tipping the scales in favor of media houses:

Cost reduction. AI VFX cuts down on outsourcing. Tasks that would go to third-party VFX houses can now be handled in-house using AI tools. There is also less reliance on stock assets because assets tailor-made for a scene can be generated. Modern AI tools that are hosted on the cloud can produce high-quality visual effects, and some can even be self-hosted with suitable hardware.
Creative freedom. Effects like automatic rotoscoping, scene segmentation, background removal, or style transfer are now more accessible. Creative ideas can be achieved without limitations due to hardware or budget ceilings.
Improved collaboration. AI tools allow for the generation of low- or mid-quality previews quickly, so editors and persons within the production pipeline can see how the VFX is looking and give feedback well before final renders.

What AI Brings to The Table

AI is revolutionizing video production by introducing innovative techniques that streamline workflows and enhance creative possibilities. The advancements that are shaping the future of VFX are:

Visual Prompts. This feature is the use of annotations and doodles to guide the model during video generation. The AI interprets these inputs to generate corresponding visual effects, allowing for intuitive and rapid prototyping without the need for detailed technical knowledge. This is becoming common in image and video generation models. AI video generation tools like Google Veo 3, Kling 2.1 Master, and Wan 2.2 can support visual prompts through providers like Freepik and Higgsfield.
Style Transfer. This feature allows creators to apply artistic styles (like cinematic, animated, or painting) to their videos. This process involves analyzing the visual elements of a reference style and adapting the video content to match, offering a seamless way to achieve desired aesthetics.
Inpainting for Video. Inpainting techniques enable the removal or replacement of unwanted elements within video frames. AI algorithms analyze surrounding pixels to fill in the gaps, ensuring continuity and realism, which is particularly useful for correcting shots or removing distractions.
Physics and Effects Simulation. AI models can simulate realistic physical phenomena (like explosions, smoke, and rain) by understanding and replicating the underlying physics. This capability allows for the creation of dynamic and immersive effects without the need for complex prod or high-end hardware.

Recommendations

Pilot AI with low-stakes footage before core scenes. Begin by using AI-generated clips for background, transitional, or establishing shots, particularly where dialogue and character focus are minimal. This approach lets you test the technology’s capabilities, pacing, and visual consistency without risking your most important narrative components.
Build a prompt library for refinement and speed. Treat each prompt as a reusable asset: document what worked well along with prompt structure, wording, and style, and note where results fell short. Over time, this evolving repository will speed up iterations and elevate output quality.
Compare tool capabilities against your project scope. When selecting an AI video generation tool, match its strengths to your production needs. For example, models like Kling 2.1 Master and Runway Gen 4 allow for a start image only as a form of input, while Google Veo 2 allows a start frame and end frame to be input for more control.
Opt for the right balance. Decide whether to use a versatile, comprehensive model to handle video generation, audio, sound, and visual effects (like Google Veo 3) or assemble a toolkit of specialized, lightweight tools tailored to each production stage. For example, one model for rough drafts and transitions, and another for polishing or enhancements.
Set quality and brand standards. Define what is acceptable visual consistency for your production in relation to resolution limits, lighting style, character fidelity, frame rate, and so on. Establishing these standards up front ensures AI-generated content aligns with your brand’s style and does not feel misplaced when joined with traditional footage.

Bottom Line

AI-generated visual effects can slash costs and speed up production for media, given the success seen with large production studios. Video editors and content creators should adopt it thoughtfully to ensure it becomes a serious production asset rather than a flashy novelty.