Phantom: Subject-Consistent Video Generation via Cross-Modal Alignment 132

Phantom is a novel video generation tool developed by ByteDance that generates videos based on facial reference images. During this process, it accurately preserves the identity features of the reference face, such as facial contours, proportions of facial features, and expressions, while also creating highly vivid video output.

What makes Phantom special is its rigorous ability to preserve individual characteristics during video generation. For example, when a user provides a photo of themselves, the character in the Phantom-generated video will not appear distorted, warped, or conflicting with other elements. This feature opens up a new way for users to create personalized videos.

Phantom's Product Features:

Identity-Preserving Video Generation

• Generates videos from facial reference images, strictly preserving the subject's identity features (such as face and posture).

• Supports dynamic generation of video content matched to the subject (such as actions and scene changes) based on prompts.

Single-Reference Subject Video Generation

• Objects: Accurately reproduces the shape and position of items in the reference image.

• Clothing/Virtual Characters: Maintains consistent clothing texture, color, and dynamic performance of virtual characters.

• Animals: Simulates animal behavior and interaction logic.

Multi-Reference Subject Video Generation

• Group Scenes: Dynamic generation of multi-person coordinated actions and conversations.

• Product Showcase: Rotation and trial-use effects of products in different scenarios.

• Virtual Try-On: Generates virtual changing and try-on effect videos after the user uploads images.

Phantom's Technical Advantages:

• High Consistency: Subject features and detailed elements remain stable throughout the video.

• Flexible Input: Supports single-image or multi-image references, lowering the barrier to user adoption.

• Cross-Modal Alignment: Achieves precise matching between images and text descriptions, enhancing generation results.

Phantom's Application Scenarios:

Content Creation

• Short Video Generation: Quickly generate personalized short videos (such as virtual hosts and short dramas).

• Advertising and Marketing: Generate dynamic demo videos for products, suitable for e-commerce and social media promotion.

Vertical Industry Applications

• Education/Training: Create virtual character teaching videos to enhance interactivity.

• Film and Entertainment: Assist in generating low-cost animation effects or virtual scenes.

• Medical/Scientific Research: Simulate surgical procedures and visualize scientific experiments.

User Personalization Needs

• Virtual Try-On: Users upload selfies to generate clothing and accessory try-on effects.

• Digital Avatar: Create virtual identities based on personal photos and enable dynamic interaction.

Phantom's technological breakthrough comes from analyzing the correlations between different types of data (such as images and scene descriptions). We have developed a new generation of image generation technology that produces visuals with a more lifelike quality. Even when only a single static photo is provided, this technology can make virtual characters move naturally—whether it's simulating a person's actions in different scenes, enabling multiple virtual characters to interact (such as multi-person conversations in the same frame or combined product displays), or achieving the effect of "users uploading a photo to try on clothes," it all becomes as intuitive as magic.

This technological capability is especially suited for scenarios that require dynamic demonstration: showing clothing fabric quality in real time during livestreams, bringing historical figures "to life" to explain knowledge in educational videos, or having products automatically demonstrate how to use them in advertisements—tasks that previously required complex production can now be accomplished efficiently in simpler ways.

GitHub：https://github.com/Phantom-video/Phantom

Paper: https://arxiv.org/abs/2502.11079