Reference2Video

Most text-to-video tools ask you to describe a scene and hope the model interprets it the way you pictured it. That works for generic B-roll and falls apart the moment the output has to contain a specific thing — your product, your character, your set. Reference2Video is built around the opposite workflow: you supply the references first, then describe what should happen. Up to nine image references, three video references and three audio references can be attached to a single generation, which is enough to lock a subject, a prop, an environment and a motion feel simultaneously rather than fighting the prompt to reproduce them. The practical difference shows up in the example work on the site. A product ad shot uses one reference for the bottle and one for the workout bench, so the object shape and strap colour survive into the final frame instead of being re-imagined. A seven-reference story scene fixes a single setting and then layers characters, props and accessories into it — the hard part being that the result stays one continuous scene rather than a set of unrelated cuts. That is the specific failure mode reference-led generation is meant to solve, and it is the reason this tool exists as its own product rather than as a prompt trick inside a general generator. Model choice is exposed rather than hidden. Seedance 2.0 handles prompt-and-reference-led motion, Kling 3.0 covers faster action and bolder camera movement, Veo 3.1 targets cinematic realism, and the image side runs GPT Image 2, Nano Banana, Nano Banana Pro and Seedream 5.0. Controls are model-aware: aspect ratio, video length from 4 to 15 seconds, 480p or 720p resolution, and reference or sound input mode change based on what the selected model actually supports, so you are not offered settings that will be silently ignored. Credits are estimated before you generate — a Seedance 2.0 Mini 16:9 five-second 720p clip is quoted at 103 credits up front — which matters when you are iterating and need to know the cost of a retry before committing to it. An AI Enhance pass can expand a thin prompt, and generations are kept in private account history so earlier work stays retrievable. It is a freemium product, so the workflow can be tested before any spend. The honest limits: outputs are short-form by design, and multi-reference generation rewards deliberate reference selection — throw in nine loosely related images and the model has too many competing signals. Used the way it is intended, with one clear subject, one clear environment and a narrow prompt, it is a genuinely different way to get controllable video than typing a paragraph and rolling the dice.