AI 레퍼런스를 비디오로 변환 - Veemo AI

The system extracts an identity embedding from your reference image -- a mathematical fingerprint of facial geometry, skin tone, hair texture, clothing details, and body proportions. This embedding is injected into every frame of the generation process, forcing the AI to reconstruct the same subject regardless of pose, lighting, or background changes. The result is a character that looks identical whether standing in a forest or walking through a neon-lit city.

Multiple references help when you need the AI to understand a subject from different angles or capture details not visible in a single shot. For example, uploading a front-facing portrait plus a side profile gives the model better 3D understanding for head-turning scenes. You can also use separate references for different subjects -- one image for the character, another for a specific outfit, and a third for the environment you want them placed in.

Sharp, well-lit images with the subject occupying at least 30% of the frame produce the strongest identity lock. Avoid group photos where the target face is small, heavily filtered selfies that distort features, or images with sunglasses or masks that hide key facial landmarks. Plain or uncluttered backgrounds help the AI isolate the subject more cleanly, though it can handle moderate background complexity.

Facial similarity typically reaches 90-95% fidelity on Kling 2.6 and Wan 2.6 models. Fine details like freckles, eye color, and jawline shape are preserved reliably. Subtle differences may appear in extreme poses (looking straight up, heavy profile angles) or when the prompt requests dramatic lighting that casts deep shadows. Running a short 5-second test generation is the fastest way to verify fidelity before producing longer content.

That is the primary use case. Upload one reference image, then generate separate videos with different scene prompts: walking through a snowy mountain trail, presenting at a corporate stage, surfing at sunset. The character's appearance stays locked while the AI builds entirely new worlds around them. Content creators use this to build serialized stories, product campaigns, or social media series with a recognizable recurring character.

Standard text-to-video generates characters from scratch each time, so the same prompt produces a different-looking person in every run. Image-to-video animates a single photo but is limited to that one scene. Reference-to-video combines the best of both: it locks a subject's identity from your reference photo, then generates entirely new scenes, actions, and environments around that locked identity. It is the only workflow that guarantees visual continuity across separate generations.