Object placement in perspective space

The problem with most AI generation workflows is that they treat each output as a standalone artifact. You generate, you evaluate, you discard or keep — and then you start again from scratch. What gets lost is the system: the relationship between prompt structure, model behavior, and the specific visual language you're trying to develop.

Building a production-level workflow means treating your prompts, references, and outputs as a connected body of work — not a series of isolated experiments. The canvas is where that system lives.

Start with the structure, not the detail

The most common mistake is front-loading specificity. Writers know this problem as trying to write perfect sentences before you have a working outline. In generative work, it shows up as obsessing over lighting descriptors when the composition itself isn't right yet.

Work in passes. Your first generation should answer only the compositional question — does the subject read clearly against the background? Is the perspective plausible? Only once the structural read is right do you start layering in lighting, texture, and material detail.

The best creative directors in this space don't think in individual prompts. They think in systems — a vocabulary of references, constraints, and combinatorial rules that produce consistent results at scale.

Perspective as a first-class constraint

Object placement is fundamentally a perspective problem. A generated scene has an implied camera position — focal length, height, angle — and any object you introduce needs to be consistent with that implied camera, or it will read as wrong even if the viewer can't immediately articulate why.

The practical approach: identify the vanishing points in your scene before you try to place anything. Describe the camera position in your prompt as concretely as you describe the subject — "shot at knee height, 35mm equivalent, slight upward tilt" gives the model something to anchor against.

Grounding with reference geometry

For product placement and architectural integration — the cases where technical accuracy matters most — a geometry pass before the generation pass makes a significant difference. Rough 3D blockouts, even at low fidelity, give the model a structural skeleton to work against.

OTOY Canvas supports this with its 3D input nodes: you can bring in a rough mesh or a splat scene and use it as structural reference, then pipe the result into an image refinement model. The perspective comes from real geometry, not from the model's guess.

The shadow test

A quick diagnostic for placement accuracy: does the shadow match? Shadows encode the light direction, the camera angle, and the relationship between the object and the ground plane. If any of those are inconsistent, the shadow will look wrong before anything else does. Use it as an early-exit check.

Building a consistent pipeline

Once you have a placement approach that works, the goal is to make it reproducible. That means saving the prompt structure, the reference set, the model configuration, and the node graph as a named workflow — not just the final output.

The workflows that scale are the ones where a new team member can open the graph, understand the intent from the structure, and produce a consistent result without asking what settings were used. That's the difference between a creative system and a lucky generation.