Josh Bohoskey on crafting the textured autumnal look on Rooster

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Created by Bill Lawrence and Matt Tarses, HBO comedy Rooster stars Steve Carell as an author teaching at an Ivy League university where his daughter is a professor.

The series blends warm autumn tones, soft blown highlights, heavy textures and deliberately imperfect contrast behaviour into an image that’s far removed from the polished Rec. 709 look associated with many contemporary sitcoms.

At the centre of that process was the collaboration between DOP Blake McClure, ASC, colourist Josh Bohoskey, who works for colour boutique Rare Medium, and LA post facility DigitalFilm Tree, where LUT design, dailies, VFX and final grading all existed within the same DaVinci Resolve ecosystem.

“This was never about making comedy look dark or moody,” Bohoskey begins. “The brief was warmth. We kept saying the show should feel like a wool sweater: comforting, nostalgic, textured.”

Initially inspired by the blown highlights and analogue texture of 2023 satire Dream Scenario, McClure and director Jonathan Krisel explored shooting Super 16 before pivoting toward large format 65mm digital capture.

The production ultimately adopted Blackmagic’s URSA Cine 17K 65, shooting primarily in 8K using the full width of the sensor within a 2:1 aspect ratio. McClure paired the camera with Camtec Falcon glass, typically working around a 55mm T1.3 focal length to preserve intimacy while exploiting the compression and spatial rendering of the large sensor.

Building the look

Bohoskey first collaborated nearly a decade ago with McClure on pre-taped shorts for Saturday Night Live. He was brought onto Rooster before principal photography began and used DigitalFilm Tree’s Resolve-based workflow to develop the show’s look during lens, wardrobe and exposure testing.

A defining ingredient of the process became the Camtec’s Color-Con filter system, which introduces coloured LED illumination directly into the lens through a matte-box-mounted diffusion system. The Color-Con alters shadow density, highlight roll-off and contrast dynamically depending on exposure.

“The Color-Cons were wild,” Bohoskey explains. “Even small exposure changes would drastically shift the contrast structure. We had to figure out where the image would generally live exposure wise so we could build LUTs around that.”

These filters introduced subtle colour contamination, ‘greens, magentas and warmth shifts’, while simultaneously exaggerating both the lens shading and vignetting across the large format 65mm image circle.

“It completely destroys the image in this incredibly beautiful way,” Bohoskey describes. “There are weird imperfections happening that you wouldn’t intentionally design. That then became part of the show’s identity.”

Blackmagic later developed a Resolve plug-in inspired by the look, but Bohoskey says the production decided to stay committed to the practical filters because of their unpredictability.

“While the plug-in could emulate it closely, the physical filters were doing these strange organic things that felt impossible to fake.”

Contrast and vignetting

The aggressive vignetting generated by the Color-Con system, compounded by the natural edge shading of the large format sensor and lenses, proved the main issue when grading. “Some shots almost looked like you were viewing the world through a pinhole,” Bohoskey says.

DigitalFilm Tree handled dailies and online finishing internally, allowing all the departments to work inside the same shared Resolve project. That was important because the dailies team initially spent time manually compensating for the different shifting vignette patterns.

“They were trying to fight every shot individually,” Bohoskey says. “So I built a reverse-vignette structure that opened the image back up enough for editorial while preserving the character of the look.

“Every shot became this push and pull between removing technical vignetting and then deciding how much creative shaping you wanted back in afterward.”

The integrated Resolve workflow meant VFX updates, editorial revisions and colour changes all appeared in real time without renders or reconforms.

“We were sharing the same Resolve project across departments,” Bohoskey notes. “There was no exporting timelines or constantly sending versions back and forth. VFX drop-ins and reframes would just appear.”

Grading 8K BRAW

Although Bohoskey had worked in Resolve for several years after leaving The Mill, Rooster represented his first major episodic production built entirely around Blackmagic Raw acquisition.

The show shot 8K BRAW using Q3 variable bit rate (VBR) compression to keep storage requirements manageable while still preserving grading flexibility. According to McClure, the resulting files were actually smaller than some recent ALEXA 35 productions.

“We were throwing everything at those images,” he says. “Grain, texture, colour separation and heavy contrast manipulation were all able to be held together beautifully.”

Despite the show’s aggressive grading approach, only one shot across all ten episodes required denoising.

Not to mention, the 8K workflow also provided considerable reframing flexibility in post, especially given the show’s unconventional large format camera. McClure positioned cameras physically closer to actors than would traditionally occur in comedy coverage, exploiting the lack of distortion and the spatial compression of the 65mm sensor.

Creating New England in California

Although Rooster is set within an Ivy League-style East Coast institution, production took place largely in Los Angeles and at the University of the Pacific campus near San Francisco. Recreating the feel of a north-eastern autumn became another collaboration between grading and VFX.

“The biggest challenge was foliage,” Bohoskey says. “We couldn’t just turn trees orange and call it done.”

VFX handled large-scale seasonal transformations and palm tree removals, while Bohoskey refined colour separation and density in the final grade. “We needed subtle variation in the leaves,” he says. “Not just orange blobs. You still needed detail and depth.”

In winter scenes, secondary grading was used to suppress California greenery and shape backgrounds toward colder north-eastern palettes. “Sometimes it was as simple as desaturating green grass in the background or isolating out-of-focus trees in order to push the environment further.”

The approach proved convincing enough that many viewers reportedly assumed the series had genuinely been shot in the Boston area.

Refining the pipeline

By the end of the first season, Bohoskey says the creative team felt they had fully discovered the show’s visual identity. “We actually went back and reviewed our favourite moments from each episode afterward. Episodes 9 and 10 especially felt like we’d really found the language.”

Season 2, which will reportedly move visually past winter and into spring, is already prompting refinements. Bohoskey is developing dailies node structures designed to carry directly into final grading sessions, reducing the need for rebuilds during finishing.

“The goal is to hit the ground running,” he says. “It’s still a comedy at the end of the day, but we wanted it to feel tactile and human, and not polished or sterile. Something warmer and more lived-in.

AI on the Virtual Stage

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Artificial intelligence is transforming film and television production, and creeping inside the LED volumes of virtual studios too. While still very much a supporting player AI is increasingly being used to streamline workflows and solve production bottlenecks — but not to replace real-world filmmaking.   

Quite Brilliant, a virtual production studio in London, have begun to augment its workflows with AI tools.

“Traditionally in virtual production we’ve used 3D games engines like Unreal,” says Ross Shaw, the studio’s Virtual Production Supervisor. “More recently, tools like Chaos Arena, which is a CGI-based backend using V-Ray, produces more realistic imagery. But even then, you still don’t quite get the exact realism you would from shooting a real plate or having a genuine environment behind the actors.”

AI sits somewhere in between. He says, “It can create very convincing imagery quickly and cost-effectively. You’re not necessarily getting full three-dimensional parallax, but you are getting something that looks far more realistic than earlier game-engine approaches.

“It’s really about delivering more for less,” Shaw says. “Clients want high production value, but they also want faster turnaround and tighter budgets. AI helps us meet those expectations.”

In advertising production, where speed is often crucial, the time savings can be significant. Quite Brilliant recently used AI-generated environments for a campaign linked to ITV’s Birthday Draw, enabling the team to film multiple locations within a single studio shoot.

“That project involved 15 different setups over five days,” Shaw says. “We could jump from a beach environment to a suburban street very quickly because the backgrounds were generated digitally.”

Gaussian splats evolve

Veles Productions, a Warsaw-based virtual production services company, estimates that certain virtual sets can now be produced at a fraction of traditional costs. What once required weeks of labour can now be achieved in hours—or even minutes—with iterative prompting.

“This is not about saving a bit of time,” CEO Artur Paprocki. “It’s a game changer. If something is ten times cheaper, there’s no argument anymore.”

At the centre of this shift is gaussian splatting, a technique that reconstructs 3D environments using dense point clouds derived from images or scans. Unlike traditional polygon-based modelling, it allows for fast, relatively lightweight scene generation with convincing depth and parallax.

“Current AI tools are not without flaws,” he says. “There are occasional hallucinations, perspective inconsistencies and limited animation and interactivity.

Yet progress is happening at an extraordinary pace. “Tools that didn’t exist in December are available now,” notes Martynian Rozwadowski, Veles’ Head of Technology. “Editing, relighting, moving objects—it’s all improving.”

The comparison to early generative AI models is telling.

“With gaussian splatting, we’re somewhere between ChatGPT 2 and v3.5,” Paprocki says. The latest version is 5.2. “But it’s moving fast.”

Last year, Veles used 3D model Marble to generate gaussian splats from a text prompt. It was believed a world first. Veles had used the technique with Unreal Engine and Veles’ virtual production stage to create a pop video for Polish singer Naczzos.

Now the studio has begun to phase Unreal Engine out of its pipeline. “If you build a scene in Unreal, you need the entire pipeline—modelling, texturing, lighting, optimisation,” says Paprocki. “That’s extremely expensive. Compared to prompting a model and placing it directly into the scene, it’s a completely different scale.”

New hybrid workflow

Convincing clients to invest in full Unreal pipelines is becoming more difficult when AI alternatives are so accessible.

“Sometimes the director doesn’t even want a full 3D scene,” explains Rozwadowski. “They just want a plate, a background image or simple animation. That can be AI-generated very easily.”

Rather than replacing existing methods outright, Veles see the future as a hybrid combining physical and virtual elements. In the foreground: real sets and actors. Midground: traditional 3D environments (e.g. Unreal Engine) and AI-generated Gaussian splat backdrops.

 “If you want interaction, you use real sets or 3D,” Paprocki says. “If you want cheap, flexible backgrounds with parallax, you use gaussian splatting. The approach is still evolving—but it represents a practical bridge between established and emerging technologies.”

The copyright Wild West

The legal framework surrounding AI-generated environments is, at best, uncertain. Paprocki likens it to a “wild west”.

When scanning real locations, existing rules still apply. Permissions may be required, particularly for recognisable buildings or protected spaces. But AI-generated environments occupy a grey area.

“If we reproduce a location by scanning, we follow the rules,” he explains. “But when AI generates a space from a prompt—how do copyrights even exist in that situation?”

For now, the company takes a pragmatic stance: act first, respond later. “If I am able to do it, I do it. If somebody has concerns, then we deal with it. It’s too early to define strict rules.”

It’s a position that reflects wider industry ambiguity, as legal systems struggle to keep pace with generative technologies.

“Many brands are concerned about how AI models are trained, so we often ‘sandbox’ the system,” Shaw explains. “Inputs and outputs stay private so the model is only trained on material we provide, rather than scraping data from the internet.

“We can’t just type prompts like: ‘Use a Canon 25mm lens and make the background look like X location.’ You have to be careful about training data, intellectual property, and how the generated image might feed back into future training datasets.”

One technique involves combining traditional 3D modelling with AI-generated textures. Artists first create simple ‘blockout’ scenes — basic geometric shapes that define the layout of buildings, streets or landscapes. AI is then used to generate the visual surfaces for those shapes, such as brickwork or vegetation.

“The structure of the scene still comes from the creative team,” Shaw says. “AI is essentially just skinning it.”

“We also create mood boards and train models on very specific references. If we’re working on a car, for example, we’ll feed the system detailed images of headlights, bodywork, and other elements so it understands that particular model.”

Some AI providers like Runway offer licensed enterprise versions with IP protection and claim its outputs are copyright-protected. “In theory that great, but it’s difficult to know how robust that protection really is especially when models may include international components or opaque training sources.”

AI expertise in demand

The integration of AI into studio environments means some studios need to equip themselves with specialists.  “It’s a real skill,” says Shaw. “There are dozens of wrappers and platforms where models can run.”

Models like Google’s AI image editor Nano Banana might appear inside other software packages such as Higgsfield or Runway so trying to use them effectively is a challenge. “Everything is evolving extremely quickly and the quality differences between them can be dramatic.”

The Veles team is candid: some roles will disappear. “If you love working in Unreal, you might have a problem,” he says. “But if you love making films, you will find a way.”

Interestingly, the team has observed that younger creatives are not always the quickest to adapt.

“We expected young people to lead this,” he notes. “But often it’s mid-career professionals—people with experience—who benefit the most. These filmmakers bring narrative expertise and are able to leverage AI as a tool rather than resist it.”

AI worldbuilding and physical lighting

When we talk about using AI‑generated worlds with physical lighting, we’re really talking about a pipeline where AI provides the structure and intent, and the lighting department turns that intent into something physically and photographically correct.

“The workflow starts much earlier than people think,” Shaw says. “We don’t necessarily just jump straight into AI imagery by using prompts (perhaps for storyboards), but we begin with 3D block-outs.”

These are usually simple geometries with rough architecture, basic horizon lines, proxy props and even just cubes and planes standing in for major shapes. Blockouts deliver true spatial relationships, for example, where light would fall, where shadows land, where occlusion might occur.

“We take the 3D layout and feed it into an AI model that can ‘skin’ the blockout, which is essentially texturing it, detailing it, proposing materials, weathering, atmospheric effects, colour grades, vegetation patterns, and surface detail,” Shaw explains. “This keeps the structure grounded while letting the AI generate the aesthetic layer.

Alternative, a storyboards can provide a visual structure reference. AI then fills in the environmental detail based on the framing, angle, and intended mood of each shot.
“Either way, the goal is the same: AI is generating a world, but the design must still come from us,” Shaw says.